Automatic evaluation method and system for enterprise bidding and tendering data

Abstract

The invention is suitable for the technical field of data identification, and provides an automatic evaluation method and system for enterprise bidding and tendering data, and the method comprises the steps: extracting text data in the enterprise bidding and tendering data; obtaining a keyword corresponding to each evaluation item, and matching a text position corresponding to the keyword in the text data; determining whether the text position is a text region corresponding to the evaluation item or not according to a context relationship of keywords in the text position; if it is determined that the text position is the text area corresponding to the evaluation item, item information located behind the keyword is extracted; and according to a preset scoring strategy corresponding to the evaluation item, scoring the item information to obtain a total score corresponding to the enterprise bidding and tendering data. According to the scheme, the accuracy and the reliability of the bid evaluation result are improved, and the efficiency and the fairness of bid evaluation work are also remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data identification, and in particular relates to a method and system for automatically evaluating enterprise bidding data. Background Art

[0002] With the continuous development of social economy, competition among enterprises is becoming increasingly fierce. As an important way for enterprises to participate in market competition, bidding is widely used in various engineering projects, material procurement, service outsourcing and other fields. In the traditional bidding process, manual evaluation requires a lot of time and energy to review and score each bidding document in detail, which is not only easily affected by human factors, resulting in subjectivity and inconsistency in scoring, but also inefficiency.

[0003] In recent years, with the rapid development of information technology and big data technology, automated bid evaluation technology has gradually become a hot topic in research and application. By automatically processing and analyzing bidding data, the efficiency and fairness of the bid evaluation process can be improved. However, the existing automated bid evaluation methods still have some shortcomings. For example, it is difficult for existing technologies to accurately extract effective evaluation information from complex text data, and they lack flexibility and intelligence in the scoring process, which affects the accuracy and reliability of the bid evaluation results. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and system for automatically evaluating enterprise bidding data to solve the technical problem of how to ensure the accuracy and reliability of evaluation results.

[0005] A first aspect of an embodiment of the present invention provides a method for automatically evaluating enterprise bidding data, the method comprising:

[0006] Extracting text data from the enterprise bidding data;

[0007] Obtaining keywords corresponding to each evaluation item, and matching text positions corresponding to the keywords in the text data;

[0008] According to the contextual relationship of the keywords in the text position, confirm whether the text position is the text area corresponding to the evaluation item;

[0009] If it is determined that the text position is the text area corresponding to the evaluation item, then extracting the item information after the keyword;

[0010] The project information is scored according to the preset scoring strategy corresponding to the evaluation item to obtain a total score corresponding to the enterprise bidding data; the total score is used to sort different enterprise bidding data.

[0011] Furthermore, the step of confirming whether the text position is a text area corresponding to the evaluation item according to the contextual relationship of the keywords in the text position includes:

[0012] If there is only one text position corresponding to the keyword, confirming that the text position is the text area corresponding to the evaluation item;

[0013] If there are multiple text positions corresponding to the keyword, then the semantic scores corresponding to the multiple text positions are calculated;

[0014] The text position corresponding to the maximum semantic score is used as the text area corresponding to the evaluation item.

[0015] Furthermore, if there are multiple text positions corresponding to the keyword, the step of calculating the semantic scores corresponding to the multiple text positions includes:

[0016] If there are multiple text positions corresponding to the keyword, then obtain the related words and standard sentence libraries corresponding to the keyword;

[0017] Constructing a first distribution feature matrix corresponding to the text position;

[0018] Constructing a second distribution feature matrix corresponding to each of the plurality of standard sentences in the standard sentence library;

[0019] Calculating the similarity between the first distribution feature matrix and the second distribution feature matrix;

[0020] The maximum value among the similarities corresponding to the plurality of standard sentences is used as the semantic score.

[0021] Furthermore, the step of constructing a first distribution feature matrix corresponding to the text position includes:

[0022] Matching the current related words existing in the text position, and calculating the first word code corresponding to the current related words;

[0023] extracting a first word distance between a keyword and a plurality of current related words in the text position;

[0024] A first distribution feature matrix is ​​constructed based on multiple first word codes and multiple first word spacings.

[0025] Furthermore, the step of constructing a first distribution feature matrix based on the plurality of first word codes and the plurality of first word spacings includes:

[0026] Obtaining a matrix template corresponding to the keyword; wherein different element positions in the matrix template correspond to first word codes and first word distances corresponding to different related words in sequence;

[0027] Matching first word codes and first word distances corresponding to a plurality of related words to corresponding element positions in the matrix template;

[0028] The positions of blank elements in the matrix template are set to fixed values ​​to obtain the first distribution feature matrix.

[0029] Furthermore, the step of scoring the project information according to the preset scoring strategy corresponding to the evaluation item to obtain the total score corresponding to the enterprise bidding data includes:

[0030] Obtaining a preset mapping table to match the sub-scores corresponding to the plurality of project information;

[0031] Multiplying the plurality of sub-scores by weights respectively to obtain a plurality of numerical values;

[0032] The sum of multiple values ​​is taken as the total score corresponding to the enterprise's bidding data.

[0033] Furthermore, after the step of taking the maximum value of the similarities corresponding to the plurality of standard sentences as the semantic score, the method further includes:

[0034] If the similarity is greater than a threshold, the maximum value among the similarities corresponding to the plurality of standard sentences is used as the semantic score;

[0035] If the similarity is not greater than the threshold, it is confirmed that the text area corresponding to the evaluation item corresponding to the similarity does not exist in the text data.

[0036] A second aspect of an embodiment of the present invention provides an automatic evaluation device for enterprise bidding data, comprising:

[0037] An extraction unit, used to extract text data from the enterprise bidding data;

[0038] An acquisition unit, configured to acquire a keyword corresponding to each evaluation item, and match a text position corresponding to the keyword in the text data;

[0039] A confirmation unit, configured to confirm whether the text position is a text area corresponding to the evaluation item according to the contextual relationship of the keywords in the text position;

[0040] A judgment unit, configured to extract the item information after the keyword if it is determined that the text position is the text area corresponding to the evaluation item;

[0041] The scoring unit is used to score the project information according to the preset scoring strategy corresponding to the evaluation item to obtain the total score corresponding to the enterprise bidding data; the total score is used to sort different enterprise bidding data.

[0042] The third aspect of an embodiment of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for automatic evaluation of enterprise bidding data described in the first aspect when executing the computer program.

[0043] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for automatic evaluation of enterprise bidding data described in the first aspect are implemented.

[0044] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: by extracting text data from bidding data and using keyword matching technology, the text area corresponding to the evaluation item is accurately located. This method reduces the error of manual recognition and improves the accuracy of information extraction. On the basis of keyword matching, the contextual relationship of keywords is further analyzed to ensure that the extracted text area is accurate. This process effectively avoids the situation of keyword misjudgment and improves the accuracy of text area confirmation. According to the preset scoring strategy, the extracted project information is automatically scored to ensure the consistency and objectivity of the scoring process. The preset scoring strategy is based on the requirements of specific evaluation items and can flexibly adapt to different bid evaluation criteria. By calculating the total score of the enterprise bidding data, the bidding data of different enterprises are sorted, and an objective and fair bid evaluation result is provided. This function not only improves the efficiency of bid evaluation work, but also reduces the possibility of human intervention, ensuring the fairness and transparency of the bid evaluation results. The automated evaluation method greatly reduces the time and energy of manual operation and improves the efficiency of the overall bid evaluation work. Bidders can focus more on optimizing the evaluation strategy and reviewing the results. Through a systematic scoring and sorting mechanism, the subjective bias that may exist in the bid evaluation process is minimized to ensure that each evaluation item can obtain a fair and impartial evaluation. This method can flexibly adjust keywords and scoring strategies according to the needs of different enterprises and projects, has strong adaptability and scalability, and can be widely used in various bidding evaluation scenarios. In summary, the automatic evaluation method for enterprise bidding data of the present invention not only improves the accuracy and reliability of the bid evaluation results through accurate information extraction, context analysis, and automated scoring and sorting, but also significantly improves the efficiency and fairness of the bid evaluation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 A schematic flow chart of an automatic evaluation method for enterprise bidding data provided by the present invention is shown;

[0047] Figure 2 A schematic diagram of an automatic evaluation device for enterprise bidding data provided by an embodiment of the present invention is shown;

[0048] Figure 3 A schematic diagram of a terminal device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0050] The embodiment of the present invention provides a method and system for automatically evaluating enterprise bidding data to solve the technical problem of how to ensure the accuracy and reliability of bidding evaluation results.

[0051] First, the present invention provides a method for automatically evaluating enterprise bidding data. Figure 1 , Figure 1 FIG. 1 is a schematic flow chart of an automatic evaluation method for enterprise bidding data provided by the present invention. Figure 1 As shown, the method for automatically evaluating enterprise bidding data may include the following steps:

[0052] Step 101: extracting text data from the enterprise bidding data;

[0053] Extract all text data from the company's bidding documents. These text data are the basis for subsequent analysis and processing.

[0054] Step 102: Obtain keywords corresponding to each evaluation item, and match text positions corresponding to the keywords in the text data;

[0055] Evaluation items include but are not limited to quotation, payment method, payment time, company qualifications, personnel qualifications, human resources, material resources and financial resources.

[0056] Different evaluation items correspond to different keywords. For example, the keywords of the evaluation item "quotation" are usually price or quotation, etc., and the keywords of the evaluation item "payment method" are payment method or settlement method, etc. The same applies to other evaluation items.

[0057] Step 103: confirming whether the text position is a text area corresponding to the evaluation item according to the contextual relationship of the keywords in the text position;

[0058] Since there may be multiple text locations for a keyword, but there is usually only one text location for the evaluation item corresponding to the keyword, it is necessary to analyze the context of the keyword location to confirm whether the text location is the text area corresponding to the evaluation item.

[0059] For example, in a bidding document, we need to find evaluation items related to "quotation". Assume that the word "quotation" may appear multiple times in the document, but only one position is the text area that actually corresponds to the evaluation item.

[0060] First, you need to search for the keyword "quotation" in the entire bidding document and record all its occurrences. For example, suppose "quotation" appears in the following places:

[0061] Page 3, paragraph 2, page 15, paragraph 3, and page 18, paragraph 1

[0062] Analyze keyword context: For each location where a “quote” appears, you need to check the surrounding text content, especially the context, to determine whether this location is relevant to the evaluation item.

[0063] For example:

[0064] The text of the second paragraph on page 3 is: "The final quotation for this project will be announced after the bid opening. All bidders must ensure the accuracy and competitiveness of their quotations."

[0065] The text of the third paragraph on page 15 is: "The bidding documents should include the following: company profile, technical proposal, quotation, project plan and other relevant documents."

[0066] The text of the first paragraph on page 18 is: "The price of this project is xxxxx million yuan (RMB)".

[0067] In the second paragraph of page 3, it is mentioned that "the final quotation will be announced after the bid opening", but it mainly explains the publication time and accuracy requirements of the quotation, rather than the specific scoring criteria or evaluation items. Therefore, this position is not the text area of ​​the evaluation items. The third paragraph of page 15 lists the contents that the bid documents should contain, including the quotation, but does not explain in detail how the quotation is evaluated or its specific scoring criteria in the bid evaluation. Therefore, this position is not the text area of ​​the evaluation items. However, the first paragraph of page 18 clearly mentions specific numbers and has other relevant words, so this position is the text area of ​​the evaluation items.

[0068] Specifically, step 103 specifically includes steps 1031 to 1033:

[0069] Step 1031: If there is only one text position corresponding to the keyword, confirm that the text position is the text area corresponding to the evaluation item;

[0070] If only one match is found in the text, then that is identified as a region of text that is relevant to the item being reviewed. In this case, since there is only one match, the system can determine its relevance without further analysis.

[0071] Step 1032: If there are multiple text positions corresponding to the keyword, then the semantic scores corresponding to the multiple text positions are calculated;

[0072] When a keyword appears multiple times in a text, that is, there are multiple matching positions, the system needs to further analyze the contextual relationship of these positions. Specifically, it calculates the semantic score of each matching position, which reflects the relevance of each position to the evaluation item. The specific calculation logic of the semantic score is as follows:

[0073] Specifically, step 1032 specifically includes steps A1 to A5:

[0074] Step A1: if there are multiple text positions corresponding to the keyword, then obtain the related words and standard sentence library corresponding to the keyword;

[0075] Related words refer to words that usually appear together with keywords. For example, the related words of the keyword "quotation" are "RMB", "US dollar" and "project", etc. Different documents have different ways of expressing words or habits, so it is necessary to preset multiple standard sentences corresponding to different expressions for subsequent semantic similarity calculations.

[0076] When a keyword appears in multiple locations in a text, the system needs to obtain words related to the keyword and a standard sentence library. The standard sentence library contains predefined standard sentences related to the evaluation item for subsequent semantic similarity calculation.

[0077] Step A2: constructing a first distribution feature matrix corresponding to the text position;

[0078] Specifically, step A2 specifically includes steps A21 to A23:

[0079] Step A21: matching the current related words existing in the text position, and calculating the first word code corresponding to the current related words;

[0080] Find words related to the keyword in the text (current related words), and then calculate the encoding of these words. Word encoding can be achieved through various methods, such as word embeddings or word embeddings technology (such as Word2Vec, GloVe, BERT, etc.). These encodings convert each word into a high-dimensional vector that represents its semantic features.

[0081] Step A22: extracting the first word distance between the keyword and a plurality of current related words in the text position;

[0082] Calculate the distance between the keyword and each current related word, that is, the word spacing. The word spacing can be the position difference of the words in the text. For example, if the keyword is in the 5th position and the related word is in the 8th position, the word spacing is 3. The word spacing reflects the relative position relationship between words, which is very important for capturing the local structure and semantic context of the text.

[0083] Step A23: constructing a first distribution feature matrix based on the plurality of first word codes and the plurality of first word spacings.

[0084] Specifically, step A23 specifically includes steps A231 to A233:

[0085] Step A231: Obtain a matrix template corresponding to the keyword; wherein different element positions in the matrix template correspond to first word codes and first word distances corresponding to different related words in sequence;

[0086] A matrix template is predefined for each keyword, and the element position of the template will be used to store the encoding and word spacing of related words. Its design purpose is to store the feature vector of related words at a specific position.

[0087] Step A232: Matching first word codes and first word distances corresponding to a plurality of related words to corresponding element positions in the matrix template;

[0088] The codes and word spacing of the found related words are filled into the corresponding positions of the matrix template. The specific method can be to sequentially put these codes and spacings into predetermined rows or columns of the matrix. For example, one row of the template may be dedicated to codes, and another row may be used for spacing.

[0089] Step A233: setting the blank element positions in the matrix template to fixed values ​​to obtain the first distribution feature matrix.

[0090] For the blank positions in the matrix template that are not filled by the codes of related words or word spacing, a fixed value (such as 0 or -1) is set to ensure the numerical integrity and consistency of the matrix. The matrix processed in this way is the first distribution feature matrix, which is ready for subsequent analysis or calculation.

[0091] In the present embodiment, by matching the current related words in the text position, and calculating the word coding of these words, combining the word spacing information between the keywords and the related words, a distribution feature matrix is ​​constructed. This method can fully capture the semantic features of the text position and effectively improve the accuracy of semantic matching. The word spacing information between the keywords and multiple current related words is extracted so that the distribution feature matrix can reflect the detailed relationship inside the text, thereby realizing the accurate analysis and understanding of the text area in fine granularity. By introducing the combination of word coding and word spacing, the distribution feature matrix constructed not only considers the information of the words themselves, but also considers the relative position relationship between the words, and enhances the system's ability to understand the text context. The distribution feature matrix based on word coding and word spacing information can more accurately capture the semantic environment of keywords, effectively reduce the error rate when keywords appear multiple times, and ensure that the recognition of text areas is more reliable. By automatically constructing the distribution feature matrix, the need for manual intervention is reduced, the overall processing efficiency is improved, and the system can quickly and accurately confirm the text area when facing a large amount of text. This technical solution can handle complex text environments, including long texts and sentences with complex structures, and ensures that a high level of recognition performance can be maintained in various text scenarios through a fine distribution feature matrix construction method. In summary, this technical solution achieves accurate recognition and semantic understanding of text areas by matching current relevant words, calculating word codes and extracting word spacing information, and constructing the first distribution feature matrix corresponding to the text position, thereby significantly improving the accuracy and efficiency of matching.

[0092] Step A3: constructing a second distribution feature matrix corresponding to each of the plurality of standard sentences in the standard sentence library;

[0093] The second distribution feature matrix is ​​constructed in the same manner as the first distribution feature matrix, and will not be described in detail here.

[0094] Step A4: calculating the similarity between the first distribution feature matrix and the second distribution feature matrix;

[0095] Similarity can be calculated by a variety of methods, such as cosine similarity, Euclidean distance, dot product, etc. The degree of similarity reflects the degree of semantic closeness between the text position and the standard sentence.

[0096] Step A5: taking the maximum value among the similarities corresponding to the plurality of standard sentences as the semantic score.

[0097] For each text position, the system selects the maximum value among all calculated similarity values ​​as the semantic score of the position. This maximum value represents the similarity between the text position and the most similar standard sentence in the standard sentence library, thus reflecting that the semantics of the position best meets the requirements of the evaluation item.

[0098] Specifically, step A5 includes: if the similarity is greater than a threshold, taking the maximum value of the similarities corresponding to the multiple standard sentences as the semantic score; if the similarity is not greater than the threshold, confirming that the text area corresponding to the evaluation item corresponding to the similarity does not exist in the text data.

[0099] In this embodiment, by obtaining the relevant words and standard sentence library of keywords and constructing the distribution feature matrix of text position and standard sentence, the semantic features of text can be fully captured, thereby greatly improving the accuracy of text matching. Using the distribution feature matrix, the system can better understand the meaning of keywords in different contexts, and accurately identify the text position that best meets the semantics of the evaluation item by calculating the similarity. By calculating the similarity between the first distribution feature matrix and multiple second distribution feature matrices, and selecting the maximum similarity as the semantic score, the problem of mismatching when the keyword appears multiple times can be effectively avoided, ensuring that the selected text area best meets the actual needs. This solution uses the construction and calculation of the standard sentence library and the distribution feature matrix to realize the automatic identification from keywords to text areas, reduce manual intervention, and improve processing efficiency. Through matrix similarity calculation, the text area that best matches the standard sentence can be quickly found in a large amount of text, which significantly optimizes the performance of the system and is suitable for large-scale text processing scenarios. This technical solution enhances the robustness of the system to noise and text variation through comprehensive analysis of multiple features, and can maintain a high accuracy rate even in complex or ambiguous text environments. In summary, this technical solution provides an efficient and accurate text region confirmation method by introducing keyword-related words and standard sentence libraries, combined with distribution feature matrix similarity calculation, which significantly improves the accuracy of text matching.

[0100] Step 1033: The text position corresponding to the maximum semantic score is used as the text region corresponding to the evaluation item.

[0101] By comparing the semantic scores of all matching positions, the position with the highest score is selected as the text region corresponding to the final evaluation item, which means that the contextual relationship of this position best meets the requirements of the evaluation item.

[0102] In the present embodiment, when the keyword appears only once in the text, the text position can be directly determined as the text area corresponding to the evaluation item, avoiding unnecessary complex calculations and improving the accuracy and efficiency of matching. When the keyword appears multiple times in the text, by calculating the semantic score of each text position, the text area that best meets the context of the evaluation item can be effectively distinguished. The text position with the maximum semantic score represents the best match, which significantly improves the selection accuracy in the case of multiple occurrences. Through the calculation of contextual relationships and semantic scores, the scheme not only relies on the simple matching of keywords, but also pays more attention to the semantic understanding of the text content, thereby achieving accurate matching at a deeper level. This method can better cope with the keyword recognition task in a complex text environment. When the keyword appears multiple times, the traditional method may be misjudged, and the technical solution can effectively reduce the misjudgment rate through the semantic scoring mechanism, ensuring that the identified text area is highly consistent with the actual needs of the evaluation item. The calculation method based on semantic scoring enables the system to automatically screen out the most relevant text area when processing a large amount of text, improves the overall processing efficiency, and reduces the need for manual intervention. In summary, this technical solution can effectively improve the accuracy and efficiency of text region matching through intelligent analysis of keyword context relationships and precise calculation of semantic scores.

[0103] Step 104: if it is determined that the text position is the text area corresponding to the evaluation item, extracting the item information after the keyword;

[0104] The position of the item information after the keyword is relatively fixed and has a fixed format. Therefore, the item information after the keyword can be extracted based on the relative position and format.

[0105] Step 105: Score the project information according to the preset scoring strategy corresponding to the evaluation item to obtain a total score corresponding to the enterprise bidding data; the total score is used to sort different enterprise bidding data.

[0106] Specifically, step 105 specifically includes steps 1051 to 1053:

[0107] Step 1051: Obtain a preset mapping table to match the sub-scores corresponding to the plurality of project information;

[0108] Obtain a preset mapping table that defines different evaluation items (such as project quality, time, cost, etc.) and their corresponding scoring criteria. Using this mapping table, map each project information (such as the quality, time, cost, etc. of a specific project) to the corresponding sub-score.

[0109] Step 1052: multiplying the plurality of sub-scores by weights respectively to obtain a plurality of numerical values;

[0110] For each sub-score, weighting is performed according to the preset weight. The weight reflects the importance of each evaluation item in the total score. Therefore, each sub-score is multiplied by the corresponding weight to obtain the weighted value.

[0111] Step 1053: The sum of the multiple values ​​is used as the total score corresponding to the enterprise bidding data.

[0112] Add up all weighted values ​​to get a total, which is the final total score of the enterprise's bidding data. This total score can be used to compare bids from different enterprises and help decision makers choose the best bidding plan.

[0113] As an optional embodiment of the present invention, in order to provide a more accurate score, the preset mapping table may be functionalized and calculated in the following manner:

[0114]

[0115] Among them, T represents the total score, w i represents the weight of the i-th evaluation item, f i (S i ) represents the evaluation function of the ith evaluation item (obtained by functionalizing the preset mapping table), M i Indicates the maximum score of the i-th evaluation item, A i It represents the adjustment factor of the i-th evaluation item, which is used to correct the weight or influence of the score. n represents the number of evaluation items.

[0116] The adjustment factor is used to modify the score weight or proportion of each evaluation item in order to more accurately reflect the importance of the evaluation item in different situations. This allows the scoring system to flexibly respond to different evaluation needs. For example, if a certain evaluation item is particularly important in a specific project, its score influence can be increased by adjusting the factor. Through different scoring strategy functions, different scoring methods can be used for different evaluation items to ensure that the scoring method of each evaluation item best reflects its characteristics and key indicators. By introducing adjustment factors and nonlinear combination functions, the system's expressiveness and adaptability are improved. Scoring strategy functions, nonlinear combination functions and correction functions can be further added or modified according to actual needs to adapt to different application scenarios.

[0117] In this embodiment, by extracting text data from the bidding data and using keyword matching technology, the text area corresponding to the evaluation item is accurately located. This method reduces the error of manual recognition and improves the accuracy of information extraction. On the basis of keyword matching, the contextual relationship of keywords is further analyzed to ensure that the extracted text area is accurate. This process effectively avoids the situation of keyword misjudgment and improves the accuracy of text area confirmation. According to the preset scoring strategy, the extracted project information is automatically scored to ensure the consistency and objectivity of the scoring process. The preset scoring strategy is based on the requirements of specific evaluation items and can flexibly adapt to different bid evaluation criteria. By calculating the total score of the enterprise bidding data, the bidding data of different enterprises are sorted, providing an objective and fair bid evaluation result. This function not only improves the efficiency of bid evaluation work, but also reduces the possibility of human intervention, ensuring the fairness and transparency of bid evaluation results. The automated evaluation method greatly reduces the time and energy of manual operation and improves the efficiency of overall bid evaluation work. Bid evaluation personnel can focus more on the optimization of evaluation strategies and the review of results. Through a systematic scoring and sorting mechanism, the subjective bias that may exist in the bid evaluation process is minimized to ensure that each evaluation item can obtain a fair and impartial evaluation. This method can flexibly adjust keywords and scoring strategies according to the needs of different enterprises and projects, has strong adaptability and scalability, and can be widely used in various bidding evaluation scenarios. In summary, the automatic evaluation method for enterprise bidding data of the present invention not only improves the accuracy and reliability of the bid evaluation results through accurate information extraction, context analysis, and automated scoring and sorting, but also significantly improves the efficiency and fairness of the bid evaluation work.

[0118] like Figure 2 The present invention provides an automatic evaluation device for enterprise bidding data, see Figure 2 , Figure 2 A schematic diagram of an automatic evaluation device for enterprise bidding data provided by the present invention is shown. Figure 2 The automatic evaluation device for enterprise bidding data shown includes:

[0119] An extraction unit 21, used to extract text data from the enterprise bidding data;

[0120] An acquisition unit 22 is used to acquire a keyword corresponding to each evaluation item, and match a text position corresponding to the keyword in the text data;

[0121] A confirmation unit 23, configured to confirm whether the text position is a text area corresponding to the evaluation item according to the contextual relationship of the keywords in the text position;

[0122] A judgment unit 24, configured to extract the item information after the keyword if it is determined that the text position is the text area corresponding to the evaluation item;

[0123] The scoring unit 25 is used to score the project information according to the preset scoring strategy corresponding to the evaluation item to obtain the total score corresponding to the enterprise bidding data; the total score is used to sort different enterprise bidding data.

[0124] The invention provides an automatic evaluation device for enterprise bidding data, which extracts text data from bidding data and uses keyword matching technology to accurately locate the text area corresponding to the evaluation item. This method reduces the error of manual recognition and improves the accuracy of information extraction. On the basis of keyword matching, the contextual relationship of keywords is further analyzed to ensure that the extracted text area is accurate. This process effectively avoids the situation of keyword misjudgment and improves the accuracy of text area confirmation. According to the preset scoring strategy, the extracted project information is automatically scored to ensure the consistency and objectivity of the scoring process. The preset scoring strategy is based on the requirements of specific evaluation items and can flexibly adapt to different bid evaluation standards. By calculating the total score of enterprise bidding data, the bidding data of different enterprises are sorted, and an objective and fair bid evaluation result is provided. This function not only improves the efficiency of bid evaluation work, but also reduces the possibility of human intervention, ensuring the fairness and transparency of bid evaluation results. The automated evaluation method greatly reduces the time and energy of manual operation and improves the efficiency of overall bid evaluation work. Bidders can focus more on optimizing evaluation strategies and reviewing results. Through a systematic scoring and sorting mechanism, the subjective bias that may exist in the bid evaluation process is minimized to ensure that each evaluation item can obtain a fair and impartial evaluation. This method can flexibly adjust keywords and scoring strategies according to the needs of different enterprises and projects, has strong adaptability and scalability, and can be widely used in various bidding evaluation scenarios. In summary, the automatic evaluation method for enterprise bidding data of the present invention not only improves the accuracy and reliability of the bid evaluation results through accurate information extraction, context analysis, and automated scoring and sorting, but also significantly improves the efficiency and fairness of the bid evaluation work.

[0125] Figure 3 FIG. 1 is a schematic diagram of a terminal device provided by an embodiment of the present invention. Figure 3 As shown, a terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as an automatic evaluation program for enterprise bidding data. When the processor 30 executes the computer program 32, the steps in each of the above-mentioned embodiments of the method for automatic evaluation of enterprise bidding data are implemented, such as Figure 1Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are realized, for example, Figure 2 Function of the unit shown.

[0126] Exemplarily, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the computer program 32 may be divided into the following specific functions of each unit:

[0127] An extraction unit, used to extract text data from the enterprise bidding data;

[0128] An acquisition unit, configured to acquire a keyword corresponding to each evaluation item, and match a text position corresponding to the keyword in the text data;

[0129] A confirmation unit, configured to confirm whether the text position is a text area corresponding to the evaluation item according to the contextual relationship of the keywords in the text position;

[0130] A judgment unit, configured to extract the item information after the keyword if it is determined that the text position is the text area corresponding to the evaluation item;

[0131] The scoring unit is used to score the project information according to the preset scoring strategy corresponding to the evaluation item to obtain the total score corresponding to the enterprise bidding data; the total score is used to sort different enterprise bidding data.

[0132] The terminal device includes but is not limited to a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of a terminal device 3 and does not constitute a limitation on the terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0133] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0134] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0135] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0136] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0137] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0138] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0139] An embodiment of the present invention provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0140] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera device / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0141] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0143] In the embodiments provided by the present invention, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed over multiple network units.

[0145] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0146] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0147] As used in the present specification and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to monitoring, depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is monitored" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is monitored" or "in response to monitoring [described condition or event]", depending on the context.

[0148] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0149] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0150] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for automatically evaluating enterprise bidding data, characterized in that: The method for automatically evaluating enterprise bidding data comprises: Extracting text data from the enterprise bidding data; Obtaining keywords corresponding to each evaluation item, and matching text positions corresponding to the keywords in the text data; According to the contextual relationship of the keywords in the text position, confirm whether the text position is the text area corresponding to the evaluation item; If it is determined that the text position is the text area corresponding to the evaluation item, then extracting the item information after the keyword; The project information is scored according to the preset scoring strategy corresponding to the evaluation item to obtain a total score corresponding to the enterprise bidding data; the total score is used to sort different enterprise bidding data.

2. The method for automatically evaluating enterprise bidding data according to claim 1, characterized in that: The step of confirming whether the text position is a text area corresponding to the evaluation item according to the contextual relationship of the keywords in the text position comprises: If there is only one text position corresponding to the keyword, confirming that the text position is the text area corresponding to the evaluation item; If there are multiple text positions corresponding to the keyword, then the semantic scores corresponding to the multiple text positions are calculated; The text position corresponding to the maximum semantic score is used as the text area corresponding to the evaluation item.

3. The method for automatically evaluating enterprise bidding data according to claim 2, characterized in that: If there are multiple text positions corresponding to the keyword, the step of calculating the semantic scores corresponding to the multiple text positions includes: If there are multiple text positions corresponding to the keyword, then obtain the related words and standard sentence libraries corresponding to the keyword; Constructing a first distribution feature matrix corresponding to the text position; Constructing a second distribution feature matrix corresponding to each of the plurality of standard sentences in the standard sentence library; Calculating the similarity between the first distribution feature matrix and the second distribution feature matrix; The maximum value among the similarities corresponding to the plurality of standard sentences is used as the semantic score.

4. The method for automatically evaluating enterprise bidding data according to claim 3, characterized in that: The step of constructing a first distribution feature matrix corresponding to the text position comprises: Matching the current related words existing in the text position, and calculating the first word code corresponding to the current related words; extracting a first word distance between a keyword and a plurality of current related words in the text position; A first distribution feature matrix is ​​constructed based on multiple first word codes and multiple first word spacings.

5. The method for automatically evaluating enterprise bidding data according to claim 4, characterized in that: The step of constructing a first distribution feature matrix based on the plurality of first word codes and the plurality of first word spacings comprises: Obtaining a matrix template corresponding to the keyword; wherein different element positions in the matrix template correspond to first word codes and first word distances corresponding to different related words in sequence; Matching first word codes and first word distances corresponding to a plurality of related words to corresponding element positions in the matrix template; The positions of blank elements in the matrix template are set to fixed values ​​to obtain the first distribution feature matrix.

6. The method for automatically evaluating enterprise bidding data according to claim 1, characterized in that: The step of scoring the project information according to the preset scoring strategy corresponding to the evaluation item to obtain the total score corresponding to the enterprise bidding data includes: Obtaining a preset mapping table to match the sub-scores corresponding to the plurality of project information; Multiplying the plurality of sub-scores by weights respectively to obtain a plurality of numerical values; The sum of multiple values ​​is taken as the total score corresponding to the enterprise's bidding data.

7. The method for automatically evaluating enterprise bidding data according to claim 3, characterized in that: After the step of taking the maximum value of the similarities corresponding to the plurality of standard sentences as the semantic score, the method further includes: If the similarity is greater than a threshold, the maximum value among the similarities corresponding to the plurality of standard sentences is used as the semantic score; If the similarity is not greater than the threshold, it is confirmed that the text area corresponding to the evaluation item corresponding to the similarity does not exist in the text data.

8. An automatic evaluation device for enterprise bidding data, characterized in that: The enterprise bidding data automatic evaluation device comprises: An extraction unit, used to extract text data from the enterprise bidding data; An acquisition unit, configured to acquire a keyword corresponding to each evaluation item, and match a text position corresponding to the keyword in the text data; A confirmation unit, configured to confirm whether the text position is a text area corresponding to the evaluation item according to the contextual relationship of the keywords in the text position; A judgment unit, configured to extract the item information after the keyword if it is determined that the text position is the text area corresponding to the evaluation item; The scoring unit is used to score the project information according to the preset scoring strategy corresponding to the evaluation item to obtain the total score corresponding to the enterprise bidding data; the total score is used to sort different enterprise bidding data.

9. A terminal device, characterized in that: The terminal device includes: a laser transmitting module, a laser receiving module, an Internet of Things module, a memory, a processor, and an automatic evaluation program for enterprise bidding data stored in the memory and executable on the processor. The automatic evaluation program for enterprise bidding data is configured to implement the steps in the automatic evaluation method for enterprise bidding data as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps in the method for automatic evaluation of enterprise bidding data as described in any one of claims 1 to 7 are implemented.

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