Engineering cost data query method using electronic informatization

Abstract

The invention belongs to the technical field of data query, and provides a project cost data query method utilizing electronic informatization, which comprises the following steps: analyzing the browsing duration difference of users on different click materials and the numerical value difference of the same parameter to obtain the initial importance of the parameter of each user; the initial importance is corrected by analyzing the difference degree of parameters between the clicked materials and the collected materials in the browsing process of the user; dividing all the users into different post clusters based on the correction importance of all the parameters of all the users; analyzing the similarity between the target user and each post cluster to obtain a reference user of the target user; and finally, performing material recommendation on the target cluster according to the reference user. According to the method, users of different posts are distinguished by correcting importance, the post cluster to which the target user belongs is judged, sample data on which collaborative filtering depends is screened, and the accuracy of a recommendation result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data query, and in particular to a method for querying engineering cost data using electronic information. Background Art

[0002] Project Costs (PC) refers to the estimated or actual construction costs of a project during the construction period, including various expenditures incurred throughout the entire process from project initiation to completion and delivery. It covers land costs, material costs, labor costs, machinery usage fees, management fees, profits, taxes and other related costs. It is not only an important part of engineering economic activities, but also an important basis for controlling project investment, evaluating economic benefits and formulating construction plans.

[0003] In order to ensure that the cost of the project is accurate and reasonable, it is necessary to query the engineering cost data. Through the query, the latest material prices, labor costs, machinery costs and quota data can be obtained, providing a reliable basis for budget preparation, cost control and quotation. In the process of engineering cost data query, in order to improve efficiency, accuracy and traceability, electronic information technology is often used to query engineering cost data. Compared with traditional query methods, electronic information technology realizes real-time query and dynamic update of multi-dimensional information such as material prices, quota data, construction technology, etc. through digital platforms and databases, and can quickly screen, classify and compare data to meet the personalized needs of projects.

[0004] However, when users query engineering cost data through the engineering cost data platform, the engineering cost data platform will match the keywords searched by the users with the names of the materials to obtain basic recommended materials, and then use the collaborative filtering algorithm to continue to recommend to the users based on the interaction between the users and the basic recommended materials. However, the job responsibilities of users who use the engineering cost data platform to query engineering cost data are different, resulting in different material parameters that the users are concerned about. Therefore, when using the collaborative filtering algorithm to make recommendations to users, inaccurate recommendation results will occur, resulting in reduced work efficiency. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for querying engineering cost data using electronic information.

[0006] According to the present invention, a method for querying engineering cost data using electronic information is provided, the method comprising: Obtain the parameters and browsing time of each user's clicked materials, as well as the collection materials; For each user, analyze the differences in browsing time of different click materials and the differences in the values ​​of the same parameter to obtain the preliminary importance of the parameter for each user; Based on any one of the parameters of each user, the difference between the clicked materials and the favorite materials of the user during browsing is analyzed, and the preliminary importance is revised to obtain the revised importance of the parameter of each user; Based on the modified importance of all the parameters of all the users, all the users are divided into different job clusters; Analyze the similarity between the target user and each of the job clusters to obtain reference users of the target user; According to the reference user, material recommendation is performed for the target cluster.

[0007] In some embodiments of the present invention, based on any one of the parameters of each user, the difference between the clicked material and the favorited material during the browsing process of the user is analyzed, and the preliminary importance is corrected to obtain the corrected importance of the parameter of each user, including: Based on any one of the parameters of each user, the difference in parameter values ​​between the clicked material and the favorited material during the browsing process of the user is analyzed to obtain the expected skewness of the parameter in the clicked material for each user; Clustering all the click materials of each user according to the browsing time of the click materials to obtain a click material cluster of each user; For any parameter of each user, the difference of the expected skewness corresponding to different click material clusters to which the parameter belongs is analyzed to obtain the important index of the parameter of each user; The preliminary importance is modified according to the importance index to obtain the modified importance of the parameter of each user.

[0008] In some embodiments of the present invention, based on any one of the parameters of each user, the difference in parameter values ​​between the clicked material and the favorited material during the browsing process of the user is analyzed to obtain the expected skewness of the parameter in the clicked material for each user, including: Based on any one of the parameters of each user, calculating the numerical mean of the parameter in all the collection materials of the user; The difference between the numerical value of the parameter in the user's click material and the numerical mean is calculated to obtain the expected skewness of each user for the parameter in the click material.

[0009] In some embodiments of the present invention, all the click materials of each user are clustered according to the browsing time of the click materials to obtain the click material cluster of each user, including: According to the browsing time of the click materials, DBSCAN clustering is performed on all the click materials of each user to obtain a click material cluster of each user, wherein each click material cluster represents the time required for the user to browse to a certain parameter.

[0010] In some embodiments of the present invention, the click material cluster of each user is obtained, and then the following steps are further included: According to the browsing time, the click material clusters are sorted in order from small to large to obtain a click material cluster sequence for each user.

[0011] In some embodiments of the present invention, for any parameter of each user, the difference of the expected skewness corresponding to different click material clusters to which the parameter belongs is analyzed to obtain the important index of the parameter of each user, including: For any parameter of each user, based on the click material cluster sequence, the difference in the discrete degree of the expected skewness corresponding to different click material clusters to which the parameter belongs is analyzed, and the possibility of each click material cluster of each user being the boundary of the parameter is obtained; According to the demarcation possibility and in combination with the position ordinal number of the click material cluster in the click material cluster sequence, the importance index of the parameter of each user is obtained.

[0012] In some embodiments of the present invention, for any parameter of each user, based on the click material cluster sequence, the difference in the discrete degree of the expected skewness corresponding to different click material clusters to which the parameter belongs is analyzed, and the possibility of each click material cluster of each user being a demarcation of the parameter is obtained, including: For any one of the parameters of each user, according to the position ordinal number of the click material cluster in the click material cluster sequence, the difference between the discrete degrees of the expected skewness corresponding to all the click material clusters before the position ordinal number and the discrete degrees of the expected skewness corresponding to all the click material clusters after the position ordinal number are analyzed, and combined with the mean of the corresponding expected skewness in all the click material clusters, the possibility of each click material cluster of each user as the demarcation of the parameter is obtained.

[0013] In some embodiments of the present invention, based on the modified importance of all the parameters of all the users, all the users are divided into different job clusters, including: According to the correction importance, all the parameters of each user are clustered by DBSCAN to obtain a number of parameter clusters corresponding to each user; Analyze the difference between the modified importance of all parameters in all parameter clusters of any two users to obtain the degree of job difference between any two users; According to the degree of job differences, DBSCAN clustering is performed on all users to obtain several job clusters.

[0014] In some embodiments of the present invention, analyzing the similarity between the target user and each of the job clusters to obtain a reference user for the target user includes: Determine whether the target user collects the material; If not, the initial importance of the parameter is used as a recommended reference index; If yes, the revised importance of the parameter is used as a recommended reference index; Calculate the mean of the modified importance of the parameters for all users in the job cluster, combine with the recommendation reference index, traverse all the parameters of the target user, and obtain the similarity between the target user and each of the job clusters; All users in the job cluster with the highest similarity to the target user are recorded as reference users of the target user.

[0015] In some embodiments of the present invention, recommending materials for the target cluster according to the reference user includes: Score each material browsed by the reference user to obtain a reference score of each material by the reference user; According to the reference score, a collaborative filtering algorithm is used to obtain the score of each material given by the target user, so as to obtain a target score of each material given by the target user; Materials are recommended to the target user in descending order of the target scores.

[0016] It can be seen from the above embodiments that the method for querying engineering cost data using electronic information provided by the embodiments of the present invention has the following beneficial effects: The present invention obtains the preliminary importance of each user's parameter by analyzing the differences in the browsing time of users for different clicked materials and the numerical differences of the same parameter; since the browsing time has little correlation with unimportant parameters during the calculation of the preliminary importance, the preliminary importance is corrected according to the difference in parameters between the clicked materials and the favorite materials during the user's browsing process to obtain the corrected importance; for the problem that users pay attention to different parameters due to their different positions, the present invention divides all users into different position clusters through the corrected importance of parameters, analyzes the similarity between the target user and each position cluster to obtain the reference user of the target user, and then screens the sample data relied on by collaborative filtering, which can effectively improve the accuracy of the recommendation results.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 paying creative work.

[0019] Figure 1 A schematic diagram of the basic flow of a method for querying engineering cost data using electronic information provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the engineering cost data query method using electronic informationization proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. Terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element.

[0022] A method for querying engineering cost data using electronic information provided by this embodiment will be described in detail below in conjunction with the accompanying drawings.

[0023] See also Figure 1 , which shows the basic process of a method for querying engineering cost data using electronic information provided by an embodiment of the present invention.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a method for querying engineering cost data using electronic information, which specifically includes the following steps: S100: Obtain the parameters of each user's clicked material and browsing time, and obtain the favorite material.

[0025] According to the keywords entered by the user on the engineering cost data platform, the materials whose names contain the keywords are obtained on the engineering cost data platform and recorded as basic recommended materials. Since basic recommended materials with high click volume often meet the needs of most users, in order to obtain the user's preference for this type of material faster and more accurately, the basic recommended materials are sorted in order from large to small according to the click volume, and the basic recommended materials are displayed in order of click volume.

[0026] At this time, the user will click on the material that may meet his or her criteria to view the details page, so all the parameters of all the materials clicked by each user after obtaining the basic recommended materials and the browsing time of each clicked material are obtained. The user may collect the materials that meet his or her criteria, so the user's collected materials and all the parameters contained in each collected material are obtained at the same time.

[0027] S200: For each user, analyze the differences in browsing time of different click materials and the differences in values ​​of the same parameter to obtain the preliminary importance of the parameter for each user.

[0028] When users click on the basic recommended materials to view them, the engineering cost data platform mainly obtains the parameters of the materials that users are concerned about from the browsing data of the basic recommended materials, and then makes more abundant and accurate recommendations to users based on the parameters of the materials that users are concerned about. When users browse the details page of the basic recommended materials, they often view the important parameters first and then the secondary parameters in the order of importance of the parameters. When the important parameters do not meet the requirements, the users will close the details page of the materials and continue to select. Therefore, when the browsing time of the details page of a certain material is short, the important parameters of the material may not meet the needs of the user; when the browsing time of the details page of a certain material is long, the important parameters of the material are more likely to meet the needs of the user; that is, for any parameter browsed by each user, when the difference in the parameter between the material with a longer browsing time and the material with a shorter browsing time is large, the parameter is more likely to be an important parameter.

[0029] Based on the above analysis, in the embodiment of the present invention, for each user, the browsing time differences of different click materials and the numerical differences of the same parameter are analyzed to obtain the preliminary importance of each user's parameter. User's The preliminary importance of the parameters is calculated as: In the formula, Indicates User's The initial importance of the parameters; Indicates All click materials of users include The number of click materials for each parameter; Indicates User's The browsing time of each clicked material; Indicates User's The browsing time of each clicked material; Indicates User's Click material The value of the parameter; Indicates User's Click material The value of the parameter; represents the linear normalization function.

[0030] In the above formula, the absolute value of the difference in the user's browsing time for different click materials and the absolute value of the numerical difference of the same parameter in different click materials are calculated. When the absolute values ​​of the two differences are large, it means that the browsing time of the parameter in different click materials is relatively different and the numerical difference is relatively large, which means that the parameter is likely to be an important parameter.

[0031] S300: Based on any parameter of each user, the difference between the materials clicked and the materials collected by the user during the browsing process is analyzed, and the preliminary importance is corrected to obtain the corrected importance of the parameter of each user.

[0032] When the user does not show a clear preference, the preliminary importance can only be obtained through the above method to make a rough judgment on the parameters that the user is concerned about. Since users will first check the important parameters, the important parameters will most likely be checked and judged, but some unimportant parameters may not be checked or rarely checked by users from beginning to end, so the correlation between browsing time and unimportant parameters is not high, that is, the preliminary importance may be biased on unimportant parameters, so it is necessary to correct the preliminary importance. When users collect materials that meet the selection criteria, the preliminary importance can be corrected according to the difference in parameters between the clicked materials and the collected materials.

[0033] Based on the above analysis, in an embodiment of the present invention, based on any parameter of each user, the difference between the materials clicked and the materials collected by the user during browsing is analyzed, and the preliminary importance is corrected to obtain the corrected importance of each user's parameter. Further including: First, based on any parameter of each user, the difference in parameter values ​​between the materials clicked and the materials collected by the user during browsing is analyzed to obtain the expected skewness of the parameters in the clicked materials by each user. The specific implementation method is as follows: based on any parameter of each user, the numerical mean of the parameters in all the collected materials of the user is calculated; the difference between the numerical value of the parameter in the clicked materials of the user and the numerical mean is calculated to obtain the expected skewness of the parameters in the clicked materials by each user.

[0034] Then, since users check and verify parameters in order from important to unimportant, for any material, when a parameter is important to the user, if the expected skewness of the parameter in the material is large, it will be excluded at the beginning, resulting in a shorter browsing time for the material; if the expected skewness of the parameter in the material is small, the parameter meets the user's requirements, and the relatively less important parameters will continue to be checked, resulting in a longer browsing time for the material. For any material, when a parameter is generally important to the user, if the expected skewness of the parameter in the material is large, it will be excluded at a relatively middle time position during the browsing time; and before this time position, since the user will filter the material according to more important parameters, the user has not yet judged that the parameter does not meet the requirements, so the expected skewness distribution of the parameter is relatively discrete; after this time position, since the parameter has been judged to meet the requirements, the expected skewness of the parameter will be relatively small. Therefore, according to the browsing time of the clicked material, all the clicked materials of each user are clustered to obtain the clicked material clusters of each user. The specific implementation method is: according to the browsing time of the click material, DBSCAN clustering is performed on all the click materials of each user to obtain the click material cluster of each user, wherein each click material cluster represents the time required for the user to browse to a certain parameter.

[0035] The click material clusters of each user are obtained, and then the following step further includes: sorting the click material clusters from small to large according to the browsing time to obtain the click material cluster sequence of each user.

[0036] Then, for any parameter of each user, the difference of expected skewness corresponding to different click material clusters to which the parameter belongs is analyzed to obtain the important index of each user's parameter. For any parameter of each user, if the discreteness of the expected skewness of the parameter in all click material clusters before a certain click material cluster is stronger, and the discreteness of the expected skewness of the parameter in all click material clusters after the click material cluster is significantly reduced, and the expected skewness of the click material cluster is significantly higher, then the click material cluster is more likely to be the click material cluster at the time position required to browse to the parameter. Therefore, the specific implementation method is as follows: For any parameter of each user, based on the click material cluster sequence, analyze the difference in the discrete degree of the expected skewness corresponding to the different click material clusters to which the parameter belongs, and obtain the demarcation possibility of each click material cluster of each user as a parameter. More specifically: for any parameter of each user, according to the position ordinal of the click material cluster in the click material cluster sequence, analyze the difference between the discrete degree of the expected skewness corresponding to all click material clusters before the position ordinal and the discrete degree of the expected skewness corresponding to all click material clusters after the position ordinal, combined with the mean of the corresponding expected skewness in all click material clusters, to obtain the demarcation possibility of each click material cluster of each user as a parameter. Construct the first Click material clusters as the first User's The calculation formula for the demarcation probability of the parameters is: In the formula, Indicates Click material clusters as the first User's The demarcation possibility of parameters; Indicates The first of all click materials in the click material cluster The mean of the expected skewness of the parameters; Indicates The first of all click materials in the click material cluster The variance of the expected skewness of the parameters; Indicates The first of all click materials in the click material cluster The variance of the expected skewness of the parameters; Indicates The number of click material clusters of each user; Indicates the first Click material clusters before Click material clusters; Indicates the first Click material clusters; represents the linear normalization function.

[0037] For The first of all click materials in the click material cluster The mean of the expected skewness of the parameters The larger the value, the more The judgment of the parameters completes the selection of the click material, indicating that the click material cluster is selected as the first User's The greater the possibility of demarcation of the parameters; , indicating the first The expected skewness of all click material clusters before the first click material cluster is the same as the dispersion of the first click material cluster. The difference between the discrete degrees of the expected skewness corresponding to all click material clusters after the first click material cluster. The larger the value, the greater the Click material clusters before The expected skewness corresponding to the parameters is highly dispersed, and After clicking on the material cluster The expected skewness corresponding to the parameter is less discrete, indicating that Click material clusters as the first User's The greater the possibility of demarcation of the parameters.

[0038] For any parameter, there is only one demarcation click material cluster corresponding to the parameter, so the demarcation probability of the real demarcation click material cluster is relatively large. If the browsing time of all click materials in the demarcation click material cluster is longer, it means that the parameter is less important. Therefore, according to the demarcation probability, combined with the position ordinal number of the click material cluster in the click material cluster sequence, the importance index of each user's parameter is obtained. Construct the first User's The important index calculation formula of each parameter is: In the formula, Indicates User's The importance index of the parameter; Indicates The number of click material clusters of each user; Indicates Click material clusters as the first User's The demarcation possibility of parameters; Indicates the first The ordinal number of the click material cluster class; Represents the activation function, which is used to Convert to probability values ​​between [0,1] and ensure that the sum of the probabilities of all elements is 1; represents the linear normalization function.

[0039] The normalized likelihood of the demarcation The ordinal numbers of the click material clusters are weighted, that is, the larger the ordinal number of the click material clusters is, the greater the corresponding demarcation possibility is, which means that the parameter is less important, and the importance index of the parameter for the user is smaller.

[0040] Finally, since the preliminary importance is more accurate in calculating important parameters, but needs to be corrected for unimportant parameters, the importance index is used to correct the preliminary importance to improve the accuracy of parameter importance judgment. Therefore, according to the importance index, the preliminary importance is corrected to obtain the corrected importance of each user's parameter. User's The calculation formula for the modified importance of a parameter is: In the formula, Indicates User's The revised importance of each parameter; Indicates User's The initial importance of the parameters; Indicates User's The importance index of a parameter.

[0041] When the correction importance of the parameter is high, that is, If it is relatively high, it means that the parameter is relatively important, and the initial importance shall prevail. If it is smaller, it should be Based on adjustments.

[0042] S400: Based on the modified importance of all parameters of all users, all users are divided into different job clusters.

[0043] Since different positions have different focuses, for example, cost estimators focus on standard unit prices and historical costs; designers focus on material specifications and brand recommendations; construction managers focus on material suppliers and delivery cycles. Therefore, for any position, the position pays much more attention to certain parameters than to other parameters, and the parameters that different positions focus on also vary greatly.

[0044] Based on the above analysis, in an embodiment of the present invention, all users are divided into different job clusters based on the modified importance of all parameters of all users. Further including: First, all parameters of each user are clustered using DBSCAN according to the modified importance, and several parameter clusters corresponding to each user are obtained.

[0045] Then, each parameter cluster represents a set of parameters that the user pays attention to to a certain extent. For any two users, if the parameter types in the parameter clusters of the two users are more similar, and the modified importance of all parameters in the parameter clusters is also more similar, it means that the two users are more likely to belong to the same position, and the degree of difference in the positions of the two users is smaller. Therefore, the difference between the modified importance of all parameters in all parameter clusters of any two users is analyzed to obtain the degree of difference in the positions of any two users. Construct the first User and The calculation formula for the degree of job difference of each user is: In the formula, Indicates User and The degree of job differentiation among users; Indicates User's The parameter cluster class and User's The number of identical parameters in the parameter clusters; Indicates User's The average value of the corrected importance of all parameters in a parameter cluster; Indicates User's The average value of the corrected importance of all parameters in a parameter cluster; Indicates The number of parameter clusters for each user; Indicates The number of parameter clusters for each user; Expressed as a natural constant An exponential function with base ; represents the linear normalization function.

[0046] For User's The parameter cluster class and User's The number of identical parameters in the parameter clusters , the larger the value is, the more similar the parameter types of the parameter clusters of the two users are, which means that the two users are more likely to belong to the same position, and the smaller the degree of difference between the positions of the two users is; Indicates User's The parameter cluster class and User's The smaller the value, the more similar the modified importance of all parameters in the parameter clusters of the two users are, which means that the two users are more likely to belong to the same position and the smaller the degree of difference in the positions of the two users is.

[0047] Finally, according to the degree of job difference, that is, taking the degree of job difference as the metric distance, DBSCAN clustering is performed on all users to obtain several job clusters, each of which represents a job.

[0048] S500: Analyze the similarity between the target user and each job cluster to obtain reference users of the target user.

[0049] When recommending materials to the target user being searched, it is necessary to infer the target user's position based on the target user's initial browsing data, and then recommend materials to the target user based on the preferences of users in the same position. When judging the possible position of the target user being searched, it is mainly based on the difference in the importance of each parameter between the target user and the users in each position cluster. The smaller the difference, the more likely it is that they are in the same position.

[0050] Based on the above analysis, in an embodiment of the present invention, the similarity between the target user and each job cluster is analyzed to obtain reference users of the target user. Further including: First, determine whether the target user collects materials; if not, use the initial importance of the parameter as the recommended reference index; if yes, use the revised importance of the parameter as the recommended reference index.

[0051] Then, the mean of the corrected importance of parameters for all users in the job cluster is calculated, combined with the recommended reference index, and all parameters of the target user are traversed to obtain the similarity between the target user and each job cluster. The similarity calculation formula between the target user and each job cluster is constructed as follows: In the formula, Indicates the target user and Similarity of job clusters; Indicates the total number of types of parameters for each click material; Indicates that the target user is The recommended reference index of the parameters (initial importance or revised importance); Indicates All users in the job cluster The mean of the corrected importance of the parameters; Expressed as a natural constant An exponential function with base .

[0052] Indicates the target user’s The recommended reference index (initial importance or revised importance) of the parameter is related to the first The smaller the difference, the more likely the target user is to belong to the job cluster, that is, the target user and the users in the job cluster are more likely to belong to the same job.

[0053] Finally, all users in the job cluster with the highest similarity to the target user are recorded as reference users of the target user.

[0054] S600: Recommend materials for the target cluster according to the reference user.

[0055] According to the reference user, the target cluster is recommended. The method further includes: first, using the browsing time, click, collection and interactive behavior of each material browsed by the reference user to score each material browsed by the reference user, and obtaining the reference score of each material by the reference user; then, according to the reference score, using the collaborative filtering algorithm to obtain the score of each material by the target user, and obtaining the target score of each material by the target user; finally, recommending materials to the target user in descending order of the target score, that is, the material with the highest score is ranked first and recommended to the target user.

[0056] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0057] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for querying engineering cost data using electronic information, characterized in that: The method comprises: Obtain the parameters and browsing time of each user's clicked materials, as well as the collection materials; For each user, analyze the differences in browsing time of different click materials and the differences in the values ​​of the same parameter to obtain the preliminary importance of the parameter for each user; Based on any one of the parameters of each user, the difference between the clicked materials and the favorited materials during the browsing process of the user is analyzed, and the preliminary importance is corrected to obtain the corrected importance of the parameter of each user; Based on the modified importance of all the parameters of all the users, all the users are divided into different job clusters; Analyze the similarity between the target user and each of the job clusters to obtain reference users of the target user; According to the reference user, material recommendation is performed for the target cluster.

2. The method for querying engineering cost data using electronic information according to claim 1 is characterized in that: Based on any one of the parameters of each user, the difference between the clicked materials and the favorite materials during the browsing process of the user is analyzed, and the preliminary importance is corrected to obtain the corrected importance of the parameter of each user, including: Based on any one of the parameters of each user, the difference in parameter values ​​between the clicked material and the favorited material during the browsing process of the user is analyzed to obtain the expected skewness of the parameter in the clicked material for each user; Clustering all the click materials of each user according to the browsing time of the click materials to obtain a click material cluster of each user; For any parameter of each user, the differences of the expected skewness corresponding to different click material clusters to which the parameter belongs are analyzed to obtain the importance index of the parameter of each user; The preliminary importance is modified according to the importance index to obtain the modified importance of the parameter of each user.

3. The method for querying engineering cost data using electronic information according to claim 2 is characterized in that: Based on any one of the parameters of each user, the difference in parameter values ​​between the clicked material and the favorited material during the browsing process of the user is analyzed to obtain the expected skewness of the parameter in the clicked material for each user, including: Based on any one of the parameters of each user, calculating the numerical mean of the parameter in all the collection materials of the user; The difference between the numerical value of the parameter in the user's click material and the numerical mean is calculated to obtain the expected skewness of each user for the parameter in the click material.

4. The method for querying engineering cost data using electronic information according to claim 2 is characterized in that: According to the browsing time of the click material, all the click materials of each user are clustered to obtain the click material cluster of each user, including: According to the browsing time of the click materials, DBSCAN clustering is performed on all the click materials of each user to obtain a click material cluster of each user, wherein each click material cluster represents the time required for the user to browse to a certain parameter.

5. The method for querying engineering cost data using electronic information according to claim 4 is characterized in that: Get the click material cluster class for each user, and then include: According to the browsing time, the click material clusters are sorted in order from small to large to obtain a click material cluster sequence for each user.

6. The method for querying engineering cost data using electronic information according to claim 5 is characterized in that: For any parameter of each user, the difference of the expected skewness corresponding to different click material clusters to which the parameter belongs is analyzed to obtain the important index of the parameter of each user, including: For any parameter of each user, based on the click material cluster sequence, the difference in the discrete degree of the expected skewness corresponding to different click material clusters to which the parameter belongs is analyzed, and the possibility of each click material cluster of each user being the boundary of the parameter is obtained; According to the demarcation possibility and in combination with the position ordinal number of the click material cluster in the click material cluster sequence, the importance index of the parameter of each user is obtained.

7. The method for querying engineering cost data using electronic information according to claim 6 is characterized in that: For any parameter of each user, based on the click material cluster sequence, the difference in the discrete degree of the expected skewness corresponding to different click material clusters to which the parameter belongs is analyzed, and the possibility of each click material cluster of each user being used as the boundary of the parameter is obtained, including: For any one of the parameters of each user, according to the position ordinal number of the click material cluster in the click material cluster sequence, the difference between the discrete degrees of the expected skewness corresponding to all the click material clusters before the position ordinal number and the discrete degrees of the expected skewness corresponding to all the click material clusters after the position ordinal number are analyzed, and combined with the mean of the corresponding expected skewness in all the click material clusters, the possibility of each click material cluster of each user as the demarcation of the parameter is obtained.

8. The method for querying engineering cost data using electronic information according to claim 1 is characterized in that: Based on the modified importance of all the parameters of all the users, all the users are divided into different job clusters, including: According to the correction importance, all the parameters of each user are clustered by DBSCAN to obtain a number of parameter clusters corresponding to each user; Analyze the difference between the modified importance of all parameters in all parameter clusters of any two users to obtain the degree of job difference between any two users; According to the degree of job differences, DBSCAN clustering is performed on all users to obtain several job clusters.

9. The method for querying engineering cost data using electronic information according to claim 1, characterized in that: Analyze the similarity between the target user and each of the job clusters to obtain reference users of the target user, including: Determine whether the target user collects the material; If not, the initial importance of the parameter is used as a recommended reference index; If yes, the revised importance of the parameter is used as a recommended reference index; Calculate the mean of the modified importance of the parameters for all users in the job cluster, combine with the recommendation reference index, traverse all the parameters of the target user, and obtain the similarity between the target user and each of the job clusters; All users in the job cluster with the highest similarity to the target user are recorded as reference users of the target user.

10. The method for querying engineering cost data using electronic information according to claim 1, characterized in that: Recommending materials for the target cluster according to the reference user includes: Score each material browsed by the reference user to obtain a reference score of each material by the reference user; According to the reference score, a score of each material given by the target user is obtained by using a collaborative filtering algorithm to obtain a target score of each material given by the target user; Materials are recommended to the target user in descending order of the target scores.

Images