Big data-based bidding and tendering data analysis method and system

Abstract

The invention relates to the technical field of data analysis, in particular to a bidding and tendering data analysis method and system based on big data, in the method, a keyword comparison method is applied to obtain different technical requirements in a bidding document, the different technical requirements are summarized, the objective technology of the bidding document is judged, and then the bidding and tendering data are analyzed through the keyword comparison method. According to the method, the paragraph of the objective technology of the bid invitation file is obtained, the parameter definition and the digital parameter corresponding to the objective technology are extracted by using a rule extraction method according to the paragraph of the objective technology of the bid invitation file, and the technical requirements in the bid invitation file are finely analyzed and summarized through a keyword comparison method, so that any important technical key point can be prevented from being omitted.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a bidding data analysis method and system based on big data. Background Art

[0002] In the field of bidding, with the development of the market and the improvement of informatization, the number and scale of bidding projects have continued to expand, resulting in a sharp increase in the number of bidding documents received by the tendering party.

[0003] During the bidding process, the tendering party usually receives a large number of bid documents. At the same time, in order to meet these technical requirements to the greatest extent and improve their competitiveness, the bidders will present multiple technical solutions in the bid documents. When the existing technologies are reviewed by the staff, if it is impossible to accurately find the technical parameters corresponding to the bidding documents, the staff will need to spend extra time to understand and analyze those technical solutions that do not meet the requirements. In fact, these solutions may not be truly competitive or cannot meet the tenderer's core needs at all, affecting the staff's accurate judgment of the degree of match between the bid documents and the bidding requirements;

[0004] At the same time, a large number of bidding documents differ in various technical parameters. Therefore, it is necessary to determine whether the bidding documents can meet the requirements of the bidding documents through scoring. The existing technology is not convenient for finding the corresponding technical parameters quickly and accurately, which affects the scoring judgment. In view of this, we propose a bidding data analysis method and system based on big data. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem of being unable to accurately find the corresponding technical parameters in the bid document and the tender document, which leads to more time required for manual verification during the bidding document scoring process and increases the possibility of subjective scoring, especially when the tendering party needs to complete the evaluation work in a short time;

[0006] To achieve the above objectives, the present invention proposes a bidding data analysis method based on big data that can accurately extract the main technical parameters of the bidding documents corresponding to the bidding documents, including the following method steps:

[0007] S1. Using web crawler technology, collect electronic bidding documents, corresponding tender documents, and different keywords related to technical requirements from the online platform, and establish a requirements dictionary;

[0008] S2. Using the keyword comparison method to obtain different technical requirements in the bidding documents, summarize the different technical requirements, determine the main technology of the bidding documents, and again use the keyword comparison method to obtain the paragraphs in the bidding documents where the main technology is located. Based on the paragraphs in the bidding documents where the main technology is located, use the rule extraction method to extract the parameter definitions and numerical parameters corresponding to the main technology;

[0009] S3. Establish a subject dictionary using the subject technology of the tender document, obtain paragraphs corresponding to the subject technology of the tender document using the subject dictionary, and extract parameter definitions and numerical parameters using the obtained paragraphs and the rule extraction method;

[0010] S4. Receive parameter definitions and numerical parameters in the bidding documents and tender documents respectively, establish a correspondence between the parameter definitions and numerical parameters through the textual distinction method, assign corresponding weights to the numerical parameters, and score the tender documents according to the weight and deviation scoring method.

[0011] The steps for using web crawler technology to collect tender documents, their corresponding bid documents, and different keywords related to technical requirements are as follows:

[0012] Web crawler technology sends acquisition requests to the network platform;

[0013] After receiving the request, the electronic bidding platform returns a response of access permission, access prohibition, or page not found;

[0014] After receiving the response of access consent, the web crawler obtains the tender documents, the corresponding bid documents and different keywords related to the technical requirements.

[0015] Keywords related to technical requirements, such as "structural safety technical requirements correspond to concrete strength, compressive strength, flexural strength, mix ratio, cement grade, etc.; waterproofing technical requirements correspond to waterproof membranes, waterproof coatings, waterproof layer thickness, waterproof grade, etc.", all collected keywords are summarized to obtain a keyword collection, which is the demand dictionary.

[0016] The steps to obtain different technical requirements of the bidding documents through keyword comparison are as follows:

[0017] Starting from the beginning of the tender document, compare the keywords in the tender document with the demand dictionary in order from left to right;

[0018] If the keyword in the bidding document is ≠ the demand dictionary, then continue to compare the demand dictionary with the next keyword in the bidding document until all the keywords in the bidding document are compared;

[0019] If the keywords in the bidding document = the demand dictionary, then the technical requirements corresponding to the keywords in the demand dictionary = the technical requirements of the current paragraph, and the technical requirements of the current paragraph and the keywords in the demand dictionary are marked, and then the comparison continues.

[0020] By comparing the keywords in the bidding documents with the demand dictionary in the above way and establishing the demand dictionary, the staff no longer need to re-analyze and interpret the judgment criteria of technical requirements every time, which reduces the time and workload of manual analysis and improves overall work efficiency.

[0021] The steps to determine the subject technology of the bidding documents are as follows:

[0022] Receive technical requirements in different sections: Among them, t n Indicates the nth technical requirement;

[0023] The frequency of each technical requirement appearing in different paragraphs of the bidding documents is summarized as follows:

[0024] If r n >r n+1 , then the technical requirement r n Ranked r n+1 Before, then r n The subject technology of the bidding documents.

[0025] The technique of determining the main idea by the frequency of its appearance in different paragraphs avoids the subjectivity and arbitrariness of human judgment.

[0026] The steps to create a subject dictionary using the bidding document subject technology are as follows:

[0027] Retrieve the keywords corresponding to the subject technology in the demand dictionary. The retrieved keywords are the keywords contained in the subject dictionary.

[0028] Compare the keywords in the bid document with the subject dictionary;

[0029] If the keyword in the bidding document is equal to the subject dictionary, the numerical parameters of the paragraph corresponding to the keyword in the bidding document are extracted through the rule extraction method, otherwise the comparison is continued.

[0030] The steps for extracting the parameter definitions and numerical parameters corresponding to the paragraphs where the main technical content of the bidding documents and the keywords of the tender documents are located using the rule extraction method are as follows:

[0031] Accept the paragraphs where the main technology and keywords of the bidding documents are located;

[0032] Numeric parameters are associated with specific punctuation and formatting within the document;

[0033] For example, numbers plus units such as "5V" and "10kg" usually indicate numerical parameters, and punctuation marks such as colons and dashes are used to introduce numerical parameters;

[0034] The specific punctuation marks and formats of the paragraph in which the subject technology is located are scanned. If a specific punctuation mark is scanned in the paragraph, the punctuation mark is used as the boundary to extract the content before and after the punctuation mark, which is the parameter definition and numerical parameter.

[0035] For example, when you see "The digital parameters of this device are as follows:", you will know that the previous content and the following content include parameter definitions and digital parameters.

[0036] The steps for parameter definition and numerical parameters extracted by rule extraction method are as follows:

[0037] Receive parameter definitions in the bidding documents and tender documents, and compare the two parameter definitions;

[0038] If the parameter definition in the bidding document equals the parameter definition in the tender document, and the characters of the corresponding numerical parameters in the parameter definition are the same, it means that the parameter definitions in the bidding document and the tender document are the same. This parameter definition will be marked to establish the correspondence between the two numerical parameters.

[0039] If the parameter definition in the bidding document ≠ the parameter definition in the tender document, the parameter definition in the tender document and the corresponding numerical parameter mark shall be used.

[0040] Compare the keywords in the requirements dictionary with the parameter definitions in the bid documents to establish an accurate matching mechanism, ensuring that only when the two are completely consistent will the corresponding numerical parameters be associated, reducing the possibility of incorrect matching;

[0041] The steps to assign weights to numerical parameters are as follows:

[0042] Receive the corresponding quantity of digital parameters in the bidding documents and tender documents;

[0043] Equally distribute weights based on multiple numerical parameters;

[0044]

[0045] Where n is the total number of digital parameters, R i is any numeric parameter weight, and

[0046] The steps for calculating the bid document score using the deviation scoring method are as follows:

[0047] Comparison of numerical parameters between bidding documents and tender documents;

[0048] If a numerical parameter in the bidding document is less than the corresponding numerical parameter in the tender document, the weight of the corresponding numerical parameter in the bidding document shall be 0;

[0049] If a numerical parameter in the bidding document is greater than or equal to the corresponding numerical parameter in the tender document, the weight of the corresponding numerical parameter in the bidding document remains unchanged. After all numerical parameters are compared, the bidding document score is calculated.

[0050] The total score of the bid document is the sum of the scores of all numerical parameters:

[0051]

[0052] Where S is the total score of the bidding document, s i Scoring of different numerical parameters of the tender documents.

[0053] After accurate matching and setting of weights, the score of each bid document is quickly calculated through the deviation scoring method, allowing the tendering party to conduct preliminary screening of a large number of bid documents in a short period of time, quickly eliminate those bid documents that obviously do not meet the requirements or have low scores, and thus concentrate on in-depth analysis and evaluation of bid documents with higher scores and greater competitiveness.

[0054] According to the bidding data analysis method based on big data, a bidding data analysis system based on big data is proposed, which includes a document acquisition module, a bidding document digital parameter analysis module, a bid document digital parameter analysis module and a bid document scoring module;

[0055] The document acquisition module uses web crawler technology to acquire electronic bidding documents, corresponding bidding documents and different keywords related to technical requirements, and establishes a demand dictionary through different keywords;

[0056] The bidding document digital parameter analysis module is used to receive the bidding document and the demand dictionary, compare the keywords in the bidding document with the demand dictionary through the keyword comparison method, determine the main requirements of the bidding text, and use the rule extraction method to extract the digital parameters corresponding to the main technology of the bidding document;

[0057] The bidding document digital parameter analysis module is used to establish a subject dictionary based on the subject requirements of the bidding text, and extract the digital parameters of the bidding document through keyword comparison method and rule extraction method;

[0058] The bidding document scoring module is used to establish a connection between the bidding document and the numerical parameters of the bidding document through a text differentiation method, and score the bidding document according to the difference and weight between the numerical parameters.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. In the bidding data analysis method and system based on big data, keywords related to technical requirements are collected through the digital parameter analysis module of the bidding documents, and a requirement dictionary is established. The requirement dictionary is compared with the keywords in the bidding documents. If the requirement dictionary = the keywords in the bidding documents, the technical requirements corresponding to the keywords in the requirement dictionary = the technical requirements of the current paragraph. At this time, the technical requirements of the current paragraph and the keywords in the requirement dictionary are marked. When the comparison is completed, the main requirements of the bidding document are determined by the technical requirements with the most technical requirements in the bidding document paragraphs. In the past, staff needed to spend a lot of time analyzing technical solutions that did not meet the requirements, but now they can directly exclude obviously irrelevant parts based on the main requirements.

[0061] 2. In the bidding data analysis method and system based on big data, a subject dictionary is established through the keywords corresponding to the main technologies of the bidding documents, and the subject dictionary is compared with the bidding documents to determine the technical requirements corresponding to the bidding documents and the bidding documents. The numerical parameters of the paragraphs corresponding to the keywords in the bidding documents and the bidding documents are extracted respectively through the rule extraction method, and the numerical parameters are compared to determine the differences in the numerical parameters. The score of the bidding document is determined according to the weights corresponding to the differences and the technical parameters. This can specifically screen out the content in the bidding documents that is truly related to the key requirements of the bidding, avoid confusion and misjudgment caused by the differences in a large number of technical parameters, and ensure that the evaluation of the technical content of the bidding documents focuses on the core requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the overall module of the present invention;

[0063] Figure 2 Schematic diagram of the overall method of the present invention.

[0064] The meaning of each number in the figure is:

[0065] 100. Document acquisition module; 200. Bidding document digital parameter analysis module; 300. Bidding document digital parameter analysis module; 400. Bidding document scoring module. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0067] The bidding data analysis method based on big data includes the following steps:

[0068] S1. Using web crawler technology, collect electronic bidding documents, corresponding tender documents, and different keywords related to technical requirements from the online platform, and establish a requirements dictionary;

[0069] S2. Using the keyword comparison method to obtain different technical requirements in the bidding documents, summarize the different technical requirements, determine the main technology of the bidding documents, and again use the keyword comparison method to obtain the paragraphs in the bidding documents where the main technology is located. Based on the paragraphs in the bidding documents where the main technology is located, use the rule extraction method to extract the parameter definitions and numerical parameters corresponding to the main technology;

[0070] S3. Establish a subject dictionary using the subject technology of the tender document, obtain paragraphs corresponding to the subject technology of the tender document using the subject dictionary, and extract parameter definitions and numerical parameters using the obtained paragraphs and the rule extraction method;

[0071] S4. Receive parameter definitions and numerical parameters in the bidding documents and tender documents respectively, establish a correspondence between the parameter definitions and numerical parameters through the textual distinction method, assign corresponding weights to the numerical parameters, and score the tender documents according to the weight and deviation scoring method.

[0072] The steps for using web crawler technology to collect tender documents, their corresponding bid documents, and different keywords related to technical requirements are as follows:

[0073] Web crawler technology sends acquisition requests to the network platform;

[0074] After receiving the request, the electronic bidding platform returns a response of access permission, access prohibition, or page not found;

[0075] After receiving the response of access consent, the web crawler obtains the tender documents, the corresponding bid documents and different keywords related to the technical requirements.

[0076] Keywords related to technical requirements, such as "structural safety technical requirements correspond to concrete strength, compressive strength, flexural strength, mix ratio, cement grade, etc.; waterproofing technical requirements correspond to waterproof membranes, waterproof coatings, waterproof layer thickness, waterproof grade, etc.", all collected keywords are summarized to obtain a keyword collection, which is the demand dictionary.

[0077] The steps to obtain different technical requirements of the bidding documents through keyword comparison are as follows:

[0078] Starting from the beginning of the tender document, compare the keywords in the tender document with the demand dictionary in order from left to right;

[0079] If the keyword in the bidding document is ≠ the demand dictionary, then continue to compare the demand dictionary with the next keyword in the bidding document until all the keywords in the bidding document are compared;

[0080] If the keywords in the bidding document = the demand dictionary, then the technical requirements corresponding to the keywords in the demand dictionary = the technical requirements of the current paragraph, and the technical requirements of the current paragraph and the keywords in the demand dictionary are marked, and then the comparison continues.

[0081] By comparing the keywords in the bidding documents with the demand dictionary in the above way and establishing the demand dictionary, the staff no longer need to re-analyze and interpret the judgment criteria of technical requirements every time, which reduces the time and workload of manual analysis and improves overall work efficiency.

[0082] The steps to determine the subject technology of the bidding documents are as follows:

[0083] Receive technical requirements in different sections: Among them, t n Indicates the nth technical requirement;

[0084] The frequency of each technical requirement appearing in different paragraphs of the bidding documents is summarized as follows:

[0085] If r n >r n+1 , then the technical requirement r n Ranked r n+1 Before, then r n The subject technology of the bidding documents.

[0086] The technique of determining the main idea by the frequency of its appearance in different paragraphs avoids the subjectivity and arbitrariness of human judgment.

[0087] The steps to create a subject dictionary using the bidding document subject technology are as follows:

[0088] Retrieve the keywords corresponding to the subject technology in the demand dictionary. The retrieved keywords are the keywords contained in the subject dictionary.

[0089] Compare the keywords in the bid document with the subject dictionary;

[0090] If the keyword in the bidding document is equal to the subject dictionary, the numerical parameters of the paragraph corresponding to the keyword in the bidding document are extracted through the rule extraction method, otherwise the comparison is continued.

[0091] The steps for extracting the parameter definitions and numerical parameters corresponding to the paragraphs where the main technical content of the bidding documents and the keywords of the tender documents are located using the rule extraction method are as follows:

[0092] Accept the paragraphs where the main technology and keywords of the bidding documents are located;

[0093] Numeric parameters are associated with specific punctuation and formatting within the document;

[0094] For example, numbers plus units such as "5V" and "10kg" usually indicate numerical parameters, and punctuation marks such as colons and dashes are used to introduce numerical parameters;

[0095] The specific punctuation marks and formats of the paragraph in which the subject technology is located are scanned. If a specific punctuation mark is scanned in the paragraph, the punctuation mark is used as the boundary to extract the content before and after the punctuation mark, which is the parameter definition and numerical parameter.

[0096] For example, when you see "The digital parameters of this device are as follows:", you will know that the previous content and the following content include parameter definitions and digital parameters.

[0097] The steps for parameter definition and numerical parameters extracted by rule extraction method are as follows:

[0098] Receive parameter definitions in the bidding documents and tender documents, and compare the two parameter definitions;

[0099] If the parameter definition in the bidding document equals the parameter definition in the tender document, and the characters of the corresponding numerical parameters in the parameter definition are the same, it means that the parameter definitions in the bidding document and the tender document are the same. This parameter definition will be marked to establish the correspondence between the two numerical parameters.

[0100] If the parameter definition in the bidding document ≠ the parameter definition in the tender document, the parameter definition in the tender document and the corresponding numerical parameter mark shall be used.

[0101] Compare the keywords in the requirements dictionary with the parameter definitions in the bid documents to establish an accurate matching mechanism, ensuring that only when the two are completely consistent will the corresponding numerical parameters be associated, reducing the possibility of incorrect matching;

[0102] The steps to assign weights to numerical parameters are as follows:

[0103] Receive the corresponding quantity of digital parameters in the bidding documents and tender documents;

[0104] Equally distribute weights based on multiple numerical parameters;

[0105]

[0106] Where n is the total number of digital parameters, R i is any numeric parameter weight, and

[0107] The steps for calculating the bid document score using the deviation scoring method are as follows:

[0108] Comparison of numerical parameters between bidding documents and tender documents;

[0109] If a numerical parameter in the bidding document is less than the corresponding numerical parameter in the tender document, the weight of the corresponding numerical parameter in the bidding document shall be 0;

[0110] If a numerical parameter in the bidding document is greater than or equal to the corresponding numerical parameter in the tender document, the weight of the corresponding numerical parameter in the bidding document remains unchanged. After all numerical parameters are compared, the bidding document score is calculated.

[0111] The total score of the bid document is the sum of the scores of all numerical parameters:

[0112]

[0113] Where S is the total score of the bidding document, s iScoring of different numerical parameters of the tender documents.

[0114] After accurate matching and setting of weights, the score of each bid document is quickly calculated through the deviation scoring method, allowing the tendering party to conduct preliminary screening of a large number of bid documents in a short period of time, quickly eliminate those bid documents that obviously do not meet the requirements or have low scores, and thus concentrate on in-depth analysis and evaluation of bid documents with higher scores and greater competitiveness.

[0115] According to the bidding data analysis method based on big data, a bidding data analysis system based on big data is proposed, which includes a document acquisition module 100, a bidding document digital parameter analysis module 200, a bid document digital parameter analysis module 300 and a bid document scoring module 400;

[0116] The document acquisition module 100 uses web crawler technology to acquire electronic bidding documents, their corresponding bid documents, and different keywords related to technical requirements, and establishes a demand dictionary based on different keywords;

[0117] The bidding document digital parameter analysis module 200 is used to receive the bidding document and the requirement dictionary, compare the keywords in the bidding document with the requirement dictionary through the keyword comparison method, determine the main requirements of the bidding text, and use the rule extraction method to extract the digital parameters corresponding to the main technology of the bidding document;

[0118] The bidding document digital parameter analysis module 300 is used to establish a subject dictionary based on the subject requirements of the bidding text, and extract the digital parameters of the bidding document through keyword comparison method and rule extraction method;

[0119] The bidding document scoring module 400 is used to establish a connection between the bidding document and the numerical parameters of the bidding document through a text differentiation method, and score the bidding document according to the difference and weight between the numerical parameters.

[0120] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A bidding data analysis method based on big data, characterized in that: The method comprises the following steps: S1. Use web crawler technology to collect electronic bidding documents, their corresponding bid documents, and different keywords related to technical requirements, and establish a requirements dictionary; S2. Using the keyword comparison method to obtain different technical requirements in the bidding documents, summarize the different technical requirements, determine the main technology of the bidding documents, and again use the keyword comparison method to obtain the paragraphs in the bidding documents where the main technology is located. Based on the paragraphs in the bidding documents where the main technology is located, use the rule extraction method to extract the parameter definitions and numerical parameters corresponding to the main technology; S3. Establish a subject dictionary using the subject technology of the tender document, obtain paragraphs corresponding to the subject technology of the tender document using the subject dictionary, and extract parameter definitions and numerical parameters using the obtained paragraphs and the rule extraction method; S4. Receive parameter definitions and numerical parameters in the bidding documents and tender documents respectively, establish a correspondence between the parameter definitions and numerical parameters through the textual distinction method, assign corresponding weights to the numerical parameters, and score the tender documents according to the weight and deviation scoring method.

2. The bidding data analysis method based on big data according to claim 1, characterized in that: The steps of using the web crawler technology in S1 to collect the bidding documents, their corresponding tender documents, and different keywords related to technical requirements are as follows: Web crawler technology sends acquisition requests to the network platform; After receiving the request, the electronic bidding platform returns a response of access permission, access prohibition, or page not found; After receiving the response of access consent, the web crawler obtains the tender documents, the corresponding bid documents and different keywords related to the technical requirements.

3. The bidding data analysis method based on big data according to claim 1 is characterized in that: The steps for obtaining different technical requirements of the bidding documents by using the keyword comparison method in S3 are as follows: Starting from the beginning of the tender document, compare the keywords in the tender document with the demand dictionary in order from left to right; If the keyword in the bidding document is ≠ the demand dictionary, then continue to compare the demand dictionary with the next keyword in the bidding document until all the keywords in the bidding document are compared; If the keywords in the bidding document = the demand dictionary, then the technical requirements corresponding to the keywords in the demand dictionary = the technical requirements of the current paragraph, and the technical requirements of the current paragraph and the keywords in the demand dictionary are marked, and then the comparison continues.

4. The bidding data analysis method based on big data according to claim 1, characterized in that: The steps for determining the subject technology of the bidding document in S3 are as follows: Receive technical requirements in different sections: Among them, t n Indicates the nth technical requirement; The frequency of each technical requirement appearing in different paragraphs of the bidding documents is summarized as follows: If r n >r n+1 , then the technical requirement r n Ranked r n+1 Before, then r n The subject technology of the bidding documents.

5. The bidding data analysis method based on big data according to claim 1 is characterized in that: The steps to create a subject dictionary using the bidding document subject technology are as follows: Retrieve the keywords corresponding to the subject technology in the demand dictionary. The retrieved keywords are the keywords contained in the subject dictionary. Compare the keywords in the bid document with the subject dictionary; If the keyword in the bidding document is equal to the subject dictionary, the numerical parameters of the paragraph corresponding to the keyword in the bidding document are extracted through the rule extraction method, otherwise the comparison is continued.

6. The bidding data analysis method based on big data according to claim 1, characterized in that: The steps of extracting the parameter definitions and numerical parameters corresponding to the paragraphs where the bidding document main technical content and bidding document keywords are located by the rule extraction method are as follows: Accept the paragraphs where the main technology and keywords of the bidding documents are located; Numeric parameters are associated with specific punctuation and formatting within the document; The specific punctuation marks and formats of the paragraph in which the subject technology is located are scanned. If a specific punctuation mark is scanned in the paragraph, the punctuation mark is used as the boundary to extract the content before and after the punctuation mark, which is the parameter definition and numerical parameter.

7. The bidding data analysis method based on big data according to claim 1 is characterized by: The steps of parameter definition and digital parameters extracted by the rule extraction method are as follows: Receive parameter definitions in the bidding documents and tender documents, and compare the two parameter definitions; If the parameter definition in the bidding document equals the parameter definition in the tender document, and the characters of the corresponding numerical parameters in the parameter definition are the same, it means that the parameter definitions in the bidding document and the tender document are the same. This parameter definition will be marked to establish the correspondence between the two numerical parameters. If the parameter definition in the bidding document ≠ the parameter definition in the tender document, the parameter definition in the tender document and the corresponding numerical parameter mark shall be used.

8. The bidding data analysis method based on big data according to claim 1 is characterized by: The steps of assigning weights to numerical parameters are as follows: Receive the corresponding quantity of digital parameters in the bidding documents and tender documents; Equally distribute weights based on multiple numerical parameters; Where n is the total number of digital parameters, R i is any numeric parameter weight, and 9. The bidding data analysis method based on big data according to claim 1, characterized in that: The steps for calculating the bid document score using the deviation scoring method are as follows: Comparison of numerical parameters between bidding documents and tender documents; If a numerical parameter in the bidding document is less than the corresponding numerical parameter in the tender document, the weight of the corresponding numerical parameter in the bidding document shall be 0; If a numerical parameter in the bidding document is greater than or equal to the corresponding numerical parameter in the tender document, the weight of the corresponding numerical parameter in the bidding document will remain unchanged. After all numerical parameters are compared, the bidding document score will be calculated. The total score of the bid document is the sum of the scores of all numerical parameters: Where S is the total score of the bidding document, s i Scoring of different numerical parameters of the tender documents.

10. A system using the bidding data analysis method based on big data according to any one of claims 1 to 9, characterized in that: It includes a document acquisition module (100), a bidding document digital parameter analysis module (200), a tender document digital parameter analysis module (300) and a tender document scoring module (400); The document acquisition module (100) uses web crawler technology to acquire electronic bidding documents, corresponding bidding documents and different keywords related to technical requirements, and establishes a demand dictionary through different keywords; The bidding document digital parameter analysis module (200) is used to receive the bidding document and the demand dictionary, compare the keywords in the bidding document with the demand dictionary through a keyword comparison method, determine the main requirements of the bidding text, and use a rule extraction method to extract the digital parameters corresponding to the main technology of the bidding document; The bidding document digital parameter analysis module (300) is used to establish a subject dictionary based on the subject requirements of the bidding text, and extract the digital parameters of the bidding document through a keyword comparison method and a rule extraction method; The bidding document scoring module (400) is used to establish a connection between the bidding document and the digital parameters of the bidding document through a text differentiation method, and to score the bidding document according to the difference and weight between the digital parameters.

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