An information technology consulting management system based on big data

Abstract

The present invention relates to the field of information technology consulting management technology, specifically to an information technology consulting management system based on big data, including: an information collection module, a demand analysis module, a preliminary screening analysis module, a secondary screening analysis module, a preferred salesperson analysis module, a display terminal and a database. By collecting the basic information corresponding to the current consultant and analyzing the tendency type of the house corresponding to the current consultant, reliable data support is provided for the matching analysis of subsequent consultants corresponding to salespeople, while making up for the problem of insufficient authenticity and reliability of salespeople in the current technology, and further providing a reliable platform for the consultation security and consultation efficiency corresponding to the current consultant.

Description

Technical Field

[0001] The present invention relates to the technical field of information technology consulting management, and in particular to an information technology consulting management system based on big data. Background Art

[0002] In recent years, with the continuous development and application of new generation information technologies such as artificial intelligence, cloud computing, and the Internet of Things, various industries in society are undergoing changes. Among them, the information technology consulting industry has shown new characteristics and new development trends.

[0003] As population density continues to grow, the demand for real estate leasing is also increasing. In current real estate leasing consultations, consultants usually connect with real estate leasing sales staff through various means. Due to the variety of connection methods, not only is the authenticity and reliability of the sales staff not guaranteed, but it also increases the complexity of consultants' screening of real estate leasing, which is not conducive to consultants obtaining corresponding consulting information in a timely manner. Summary of the invention

[0004] The purpose of the present invention is to provide an information technology consulting management system based on big data to solve the problems raised by the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An information technology consulting management system based on big data, comprising:

[0006] The information collection module is used to collect the basic information corresponding to the current consultant, and obtain the basic information corresponding to the current consultant, wherein the basic information includes: gender, place of origin, wherein the place of origin is local and out-of-town.

[0007] The demand analysis module is used to obtain the housing types required by the current consultant, where the required housing types include housing types, apartment types and shop types. And analyze the preferred housing types of the current consultant.

[0008] As a further improvement of the present invention, the tendency type of the house corresponding to the current consultant is analyzed, and the specific analysis steps are as follows:

[0009] 101: extracting the housing type corresponding to the current consultant from the demand information corresponding to the current consultant, if the housing type corresponding to the current consultant is a residential type, executing step 102, if the housing type corresponding to the current consultant is an apartment type, executing step 104, if the housing type corresponding to the current consultant is a shop type, executing step 105;

[0010] 102: Obtain the required rental type corresponding to the current consultant. If the required rental type corresponding to the current consultant is the whole rental type, execute step 102-1; if the required rental type corresponding to the current consultant is the shared rental type, execute step 102-2;

[0011] 102-1: Obtain the preferred housing layout corresponding to the current consultant, and match the preferred housing layout corresponding to the current consultant with the layout influence factors corresponding to the set housing layouts to obtain the layout influence factors corresponding to the current consultant, obtain the preferred rental price range corresponding to the current consultant, thereby analyzing and obtaining the rental type influence index corresponding to the current consultant, and executing steps 1-3 at the same time;

[0012] 102-2: Obtain the co-renting conditions corresponding to the current consultant, and match the co-renting conditions corresponding to the current consultant with the co-renting influence factors corresponding to the set co-renting conditions to obtain the co-renting influence factors corresponding to the current consultant, obtain the preferred rental price range corresponding to the current consultant, thereby analyzing and obtaining the rental type influence index corresponding to the current consultant, and executing step 103 at the same time;

[0013] 103: Analyze and obtain the housing type influence coefficient corresponding to the current consultant, and execute step 106;

[0014] 104: Obtain the preferred price range of water and electricity charges corresponding to the current consultant, obtain the preferred rental price range corresponding to the current consultant, and thereby analyze and obtain the housing type influence coefficient corresponding to the current consultant, and execute step 106 at the same time;

[0015] 105: Obtain the preferred area of ​​the shop and the preferred floor of the commodity corresponding to the current consultant, thereby analyzing and obtaining the housing type influence coefficient corresponding to the current consultant, and executing step 106;

[0016] 106: Based on the demand housing type corresponding to the current consultant, it is matched with the preference parameters corresponding to each demand housing type, and the preference parameters corresponding to the current consultant are obtained. At the same time, the housing type influence coefficient corresponding to the current consultant is matched with the housing type influence coefficient threshold corresponding to each preference type in the corresponding preference parameters, and the preference type of the housing corresponding to the current consultant is obtained.

[0017] The preliminary screening and analysis module is used to analyze the preliminary selected salespersons corresponding to the current consultant to obtain a set of preliminary selected salespersons corresponding to the current consultant.

[0018] As a further improvement of the present invention, the preliminary selected salesperson corresponding to the current consultant is analyzed, and the specific analysis process is as follows:

[0019] 2-1: Extract the basic information of each house salesperson from the database, and extract the gender and patience of each house salesperson from it, and extract the gender and region of the current consultant from the basic information of the current consultant;

[0020] 2-2: Perform matching analysis on the basic information corresponding to each house salesperson and the basic information corresponding to the current consultant, and obtain the basic information matching index of each house salesperson and the current consultant;

[0021] 2-3: Compare the basic matching index of each house salesperson corresponding to the current consultant with the set basic matching index threshold. If the basic matching index of a house salesperson corresponding to the current consultant is greater than the set basic matching index threshold, the house salesperson is recorded as a designated salesperson, otherwise it is recorded as a eliminated salesperson, thereby obtaining each designated salesperson;

[0022] 2-4: Extract the house sales type corresponding to each house salesperson from the database, and based on this, extract the house sales type corresponding to each designated salesperson, and match it with the tendency type of the house corresponding to the current consultant. If the house sales type corresponding to a designated salesperson matches the tendency type of the house corresponding to the current consultant successfully, then the designated salesperson is recorded as the preliminary salesperson, thereby obtaining the preliminary salespersons corresponding to the current consultant, forming a set of preliminary salespersons corresponding to the current consultant.

[0023] The secondary screening and analysis module is used to obtain the preferred leasing area corresponding to the current consultant, and thereby analyze the second salesperson corresponding to the current consultant to obtain the set of second salespersons corresponding to the current consultant.

[0024] As a further improvement of the present invention, the second salesperson corresponding to the current consultant is analyzed, and the specific analysis steps are as follows:

[0025] 3-1: extracting the work information corresponding to each house salesperson from the database, and extracting the work information corresponding to each preliminary selected salesperson from the work information corresponding to each house salesperson;

[0026] 3-2: Extract the number of remaining houses corresponding to each rental area of ​​each preliminary salesperson from the work information corresponding to each preliminary salesperson, and based on the preferred rental area corresponding to the current consultant, select the number of remaining houses in the preferred rental area of ​​each preliminary salesperson from the number of remaining houses in each rental area corresponding to each preliminary salesperson, and record it as the number of remaining houses in the matching area corresponding to each preliminary salesperson;

[0027] 3-3: Extracting the corresponding working years of each preliminary salesperson from the corresponding working information of each preliminary salesperson, and matching the corresponding working years of each preliminary salesperson with the set reference total transaction volume corresponding to each working year, to obtain the corresponding reference total transaction volume of each preliminary salesperson;

[0028] 3-4: The preference index of each preliminary salesperson corresponding to the current consultant is analyzed, and the preference index of each preliminary salesperson corresponding to the current consultant is compared with the set reference preference index threshold. If the preference index of a preliminary salesperson corresponding to the current consultant is greater than the set reference preference index threshold, the preliminary salesperson is recorded as the second salesperson, thereby obtaining each second salesperson as the set of second salespersons corresponding to the current consultant.

[0029] The preferred salesperson analysis module is used to analyze the preferred salespersons corresponding to the current consultant to obtain the preferred salespersons corresponding to the current consultant.

[0030] As a further improvement of the present invention, each preferred salesperson corresponding to the current consultant is analyzed, and the specific analysis method is as follows:

[0031] The preferred indexes of the second salespersons corresponding to the current consultant are arranged in descending order, and a set number of salespersons are selected from the first one in turn as preferred salespersons, thereby obtaining the preferred salespersons corresponding to the current consultant.

[0032] The display terminal is used to display the preferred salespersons corresponding to the current consultant.

[0033] The database is used to store the basic information, house sales type, and work information corresponding to each house salesperson.

[0034] Beneficial effects of the present invention:

[0035] The present invention collects basic information corresponding to the current consultant and analyzes the preference type of the house corresponding to the current consultant, thereby providing reliable data support for the subsequent matching analysis of consultants and salesmen, and at the same time makes up for the lack of authenticity and reliability of salesmen in the current technology, and further provides a reliable platform for the consultation security and efficiency corresponding to the current consultant.

[0036] The present invention matches and analyzes the basic information corresponding to the current consultant and the basic information corresponding to each house salesperson, and then obtains the preliminary selected salesperson corresponding to the current consultant through screening and analysis, thereby reducing the complexity and difficulty of the current consultant's screening of real estate leasing, further ensuring the authenticity and reliability of the current consultant's corresponding consulting information, and reducing the possibility that the current consultant has a poor corresponding real estate leasing experience.

[0037] The present invention obtains the preferred leasing area corresponding to the current consultant, and based on this, combines the work information of each preliminary salesperson to analyze the preferred index corresponding to each preliminary salesperson, and further screens each preferred salesperson. This not only helps the current consultant to obtain corresponding consulting information in a timely manner, improves the timeliness of the current consultant's response to consulting questions, but also provides targeted matching needs for the current consultant's corresponding real estate leasing consultation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] See also Figure 1 As shown, the present invention is an information technology consulting management system based on big data, including: an information collection module, a demand analysis module, a preliminary screening analysis module, a secondary screening analysis module, a preferred salesperson analysis module, a display terminal and a database.

[0042] The information collection module is used to collect the basic information corresponding to the current consultant, and obtain the basic information corresponding to the current consultant, wherein the basic information includes: gender, place of origin, wherein the place of origin is local and out-of-town.

[0043] The demand analysis module is used to obtain the housing type corresponding to the current consultant's demand, where the housing type is housing type, apartment type and shop type. And analyze the current consultant's corresponding housing preference type. The specific analysis steps are as follows:

[0044] 101: Extract the housing type required by the current consultant from the demand information corresponding to the current consultant. If the housing type required by the current consultant is a residential type, execute step 102; if the housing type required by the current consultant is an apartment type, execute step 104; if the housing type required by the current consultant is a shop type, execute step 105.

[0045] 102: Obtain the required leasing type corresponding to the current consultant. If the required leasing type corresponding to the current consultant is the whole leasing type, execute step 102-1; if the required leasing type corresponding to the current consultant is the shared leasing type, execute step 102-2.

[0046] 102-1: 102-11: Get the preferred house layout corresponding to the current consultant, and match the preferred house layout corresponding to the current consultant with the layout influence factors corresponding to the set house layouts to get the layout influence factors corresponding to the current consultant , among which preferred house layouts include but are not limited to: two bedrooms and one living room, one bedroom and one living room, single room, and three bedrooms and two living rooms.

[0047] 102-12: Get the preferred rental price range corresponding to the current consultant, and extract the maximum preferred rental price corresponding to the current consultant and minimum preferred rental price , normalize the maximum preferred rental price and the minimum preferred rental price corresponding to the current consultant, and take their numerical values.

[0048] 102-13: According to the formula Calculate the leasing type impact index corresponding to the current consultant , a1 and a2 represent the price influencing factors of the maximum preferred rental price and the minimum preferred rental price corresponding to the set whole-lease type, respectively. Step 103 is executed at the same time.

[0049] 102-2: 102-21: Get the co-living conditions corresponding to the current consultant, and match the co-living conditions corresponding to the current consultant with the co-living impact factors corresponding to the set co-living conditions to get the co-living impact factors corresponding to the current consultant The conditions for sharing a house include: the roommate is a male, the roommate is a female, and the roommate is not limited.

[0050] 102-22: Get the preferred rental price range corresponding to the current consultant, and extract the maximum preferred rental price corresponding to the current consultant and minimum preferred rental price , normalize the maximum preferred rental price and the minimum preferred rental price corresponding to the current consultant, and take their numerical values.

[0051] 102-23: According to the formula Calculate the leasing type impact index corresponding to the current consultant , a3 and a4 represent the price influencing factors of the maximum preferred rental price and the minimum preferred rental price corresponding to the set shared rental type, respectively. Step 103 is executed at the same time.

[0052] 103: If the rental type required by the current consultant is the whole rental type, the rental type impact index corresponding to the current consultant in step 102-13 is used as the housing type impact coefficient corresponding to the current consultant. If the rental type required by the current consultant is the shared rental type, the rental type impact index corresponding to the current consultant in step 102-23 is used as the housing type impact coefficient corresponding to the current consultant.

[0053] 104: 104-1: Get the current consultant's preferred price range for water and electricity charges, and extract the current consultant's preferred maximum price for water and electricity charges , tend to the minimum price ; Get the preferred rental price range corresponding to the current consultant, and extract the preferred maximum rental price corresponding to the current consultant , tend to have the lowest rental price .

[0054] 104-2: Normalize the current consultant's maximum price, minimum price, maximum rental price, and minimum rental price for water and electricity charges and take their values. At the same time, according to the formula Calculate the housing type impact coefficient corresponding to the current consultant , a5, a6, a7, and a8 respectively represent the set price proportional factors.

[0055] 104-3: Execute step 106.

[0056] 105: 105-1: Get the preferred area of ​​the shop corresponding to the current consultant , Product preference floor .

[0057] 105-2: Normalize the shop preference area and shop preference floor corresponding to the current consultant and take their values. At the same time, according to the formula Calculate the house type impact coefficient corresponding to the current consultant , b1 and b2 represent the set weight factors respectively.

[0058] 105-3: Execute step 106.

[0059] 106: 106-1: Based on the demand housing type corresponding to the current consultant, it is matched with the set tendency parameters corresponding to each demand housing type to obtain the matching tendency parameters corresponding to the current consultant, wherein the tendency parameters are specifically the housing type influence coefficient thresholds corresponding to each tendency type.

[0060] 106-2: Match the housing type influence coefficient corresponding to the current consultant with the housing type influence coefficient threshold corresponding to each tendency type in the corresponding matching tendency parameter to obtain the tendency type of the housing corresponding to the current consultant.

[0061] In a specific embodiment, the present invention collects basic information corresponding to the current consultant and analyzes the preference type of the house corresponding to the current consultant, thereby providing reliable data support for the subsequent matching analysis of consultants and salesmen, and at the same time makes up for the lack of authenticity and reliability of salesmen in the current technology, and further provides a reliable platform for the consultation security and efficiency corresponding to the current consultant.

[0062] The preliminary screening analysis module is used to analyze the preliminary selected salespersons corresponding to the current consultant. The specific analysis process is as follows:

[0063] 2-1: Extract the basic information of each house salesperson from the database, and extract the gender and patience of each house salesperson. The patience of each house salesperson is obtained by obtaining the historical evaluation of each house salesperson, which includes the patience score of the house salesperson, and averaging the patience scores of each house salesperson in the historical evaluation to obtain the average patience score of each house salesperson, which is used as the patience of each house salesperson. And extract the gender and region of the current consultant from the basic information of the current consultant.

[0064] 2-2: z1: Match the gender of each house salesperson with the gender of the current consultant. If the gender of a house salesperson matches the gender of the current consultant, extract the gender influence factor of the set matching gender to obtain the gender influence factor of the house salesperson. Otherwise, extract the gender influence factor of the unmatched gender, and thus obtain the gender influence factor of each house salesperson corresponding to the current consultant. .

[0065] z2: Extract the location corresponding to the current consultant. If the location corresponding to the current consultant is local, extract the reference patience of the local salesperson. If the location corresponding to the current consultant is out of town, extract the reference patience of the out-of-town salesperson. Thus, the reference patience of the current consultant for house sales is obtained. .

[0066] z3: According to the formula Calculate the basic information matching index of each house salesperson corresponding to the current consultant, It is represented as the matching index of the i-th house salesperson to the current consultant, i is the number of the house salesperson, i=1,2,...,n, It is represented by the patience corresponding to the i-th house salesperson, Expressed as the set weight factor, Expressed as a natural constant.

[0067] 2-3: Compare the basic matching index of each house salesperson corresponding to the current consultant with the set basic matching index threshold. If the basic matching index of a house salesperson corresponding to the current consultant is greater than the set basic matching index threshold, the house salesperson will be recorded as the designated salesperson, otherwise it will be recorded as a eliminated salesperson, thus obtaining the designated salespersons.

[0068] 2-4: Extract the house sales type corresponding to each house salesperson from the database, and based on this, extract the house sales type corresponding to each designated salesperson, and match it with the tendency type of the house corresponding to the current consultant. If the house sales type corresponding to a designated salesperson matches the tendency type of the house corresponding to the current consultant successfully, then the designated salesperson is recorded as the preliminary salesperson, thereby obtaining the preliminary salespersons corresponding to the current consultant, forming a set of preliminary salespersons corresponding to the current consultant.

[0069] In a specific embodiment, the present invention matches and analyzes the basic information corresponding to the current consultant and the basic information corresponding to each house salesperson, and then obtains the preliminary selected salesperson corresponding to the current consultant through screening analysis, thereby reducing the complexity and difficulty of the current consultant's screening of real estate rentals, further ensuring the authenticity and reliability of the current consultant's corresponding consulting information, and reducing the possibility of the current consultant having a poor corresponding real estate rental experience.

[0070] The secondary screening analysis module is used to obtain the preferred leasing area corresponding to the current consultant, and analyze the second salesperson corresponding to the current consultant. The specific analysis process is as follows:

[0071] 3-1: Extract the work information corresponding to each house salesperson from the database, where the work information includes years of work experience, historical total transaction volume, and the number of remaining houses corresponding to each rental area. And extract the work information corresponding to each preliminary salesperson from the work information corresponding to each house salesperson.

[0072] 3-2: Extract the number of remaining houses corresponding to each rental area of ​​each preliminary salesperson from the work information corresponding to each preliminary salesperson, and based on the preferred rental area corresponding to the current consultant, select the number of remaining houses in the preferred rental area of ​​each preliminary salesperson from the number of remaining houses in each rental area corresponding to each preliminary salesperson, and record it as the number of remaining houses in the matching area corresponding to each preliminary salesperson , j represents the number of each preliminary selected salesperson, j=1,2,...,m.

[0073] 3-3: Extract the corresponding working years of each preliminary salesperson from the corresponding working information of each preliminary salesperson, and match the corresponding working years of each preliminary salesperson with the set reference total transaction volume corresponding to each working year, to obtain the corresponding reference total transaction volume of each preliminary salesperson At the same time, the historical total transaction volume corresponding to each preliminary salesperson is extracted from the work information corresponding to each preliminary salesperson .

[0074] 3-4: Normalize the number of remaining houses in the matching area of ​​each preliminary salesperson and take its value. At the same time, according to the formula Calculate the preferred index of each preliminary salesperson corresponding to the current consultant , b1 and b2 represent the weight factors corresponding to the set number of remaining houses and the total historical transaction volume respectively.

[0075] 3-5: Compare the preference index of each preliminary salesperson corresponding to the current consultant with the set reference preference index threshold. If the preference index of a preliminary salesperson corresponding to the current consultant is greater than the set reference preference index threshold, the preliminary salesperson will be recorded as the second salesperson. The second salespersons are obtained as the set of second salespersons corresponding to the current consultant.

[0076] The preferred salesperson analysis module is used to analyze the preferred salespersons corresponding to the current consultant. The specific analysis method is: the preferred indexes of the second salespersons corresponding to the current consultant are arranged in order from large to small, and a set number of salespersons are selected from the first one in turn as preferred salespersons, thereby obtaining the preferred salespersons corresponding to the current consultant.

[0077] In a specific embodiment, the present invention obtains the preferred leasing area corresponding to the current consultant, and based on this, combines the work information of each preliminary salesperson to analyze the preferred index corresponding to each preliminary salesperson, and further screens each preferred salesperson. This not only helps the current consultant to obtain corresponding consulting information in a timely manner, improves the timeliness of the current consultant's response to consulting questions, but also provides targeted matching needs for the current consultant's corresponding real estate leasing consultation.

[0078] The display terminal is used to display the preferred salespersons corresponding to the current consultant.

[0079] The database is used to store the basic information, house sales type, and work information corresponding to each house salesperson.

[0080] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. An information technology consulting management system based on big data, characterized in that: include: The information collection module is used to collect the basic information corresponding to the current consultant and obtain the basic information corresponding to the current consultant; The demand analysis module is used to obtain the housing type corresponding to the current consultant's demand and analyze the housing type preferred by the current consultant; The preliminary screening analysis module is used to analyze the preliminary selected salespersons corresponding to the current consultant and obtain the preliminary selected salesperson set corresponding to the current consultant; The secondary screening analysis module is used to obtain the preferred leasing area corresponding to the current consultant, and thereby analyze the second salesperson corresponding to the current consultant to obtain the set of second salespersons corresponding to the current consultant; The preferred salesperson analysis module is used to analyze the preferred salespersons corresponding to the current consultant to obtain the preferred salespersons corresponding to the current consultant; The display terminal is used to display the preferred salespersons corresponding to the current consultant; The specific analysis steps for analyzing the preference type of the current consultant for the corresponding house are as follows: Step 101: extracting the housing type corresponding to the current consultant from the demand information corresponding to the current consultant. If the housing type corresponding to the current consultant is a residential type, execute step 102; if the housing type corresponding to the current consultant is an apartment type, execute step 104; if the housing type corresponding to the current consultant is a shop type, execute step 105; Step 102: Obtain the required rental type corresponding to the current consultant. If the required rental type corresponding to the current consultant is the whole rental type, execute step 102-1; if the required rental type corresponding to the current consultant is the shared rental type, execute step 102-2; Step 102-1: Obtain the preferred house layout corresponding to the current consultant, and match the preferred house layout corresponding to the current consultant with the layout influence factors corresponding to the set house layouts to obtain the layout influence factors corresponding to the current consultant, obtain the preferred rental price range corresponding to the current consultant, and thereby analyze and obtain the rental type influence index corresponding to the current consultant, specifically: obtain the preferred house layout corresponding to the current consultant, and match the preferred house layout corresponding to the current consultant with the layout influence factors corresponding to the set house layouts to obtain the layout influence factors corresponding to the current consultant ; Get the preferred rental price range corresponding to the current consultant, and extract the maximum preferred rental price corresponding to the current consultant and minimum preferred rental price , normalize the maximum preferred rental price and the minimum preferred rental price corresponding to the current consultant, and take their values; According to the formula Calculate the leasing type impact index corresponding to the current consultant , a1 and a2 represent the price influencing factors of the maximum preferred rental price and the minimum preferred rental price corresponding to the set whole-lease type respectively; and step 103 is executed at the same time; Step 102-2: Obtain the co-renting conditions corresponding to the current consultant, and match the co-renting conditions corresponding to the current consultant with the co-renting impact factors corresponding to the set co-renting conditions to obtain the co-renting impact factors corresponding to the current consultant, obtain the preferred rental price range corresponding to the current consultant, and thereby analyze and obtain the rental type impact index corresponding to the current consultant, specifically: obtain the co-renting conditions corresponding to the current consultant, and match the co-renting conditions corresponding to the current consultant with the co-renting impact factors corresponding to the set co-renting conditions to obtain the co-renting impact factors corresponding to the current consultant , obtain the preferred rental price range corresponding to the current consultant, and extract the maximum preferred rental price corresponding to the current consultant and minimum preferred rental price , normalize the maximum and minimum preferred rental prices corresponding to the current consultant, and take their values, according to the formula Calculate the leasing type impact index corresponding to the current consultant , a3 and a4 respectively represent the price influencing factors of the maximum preferred rental price and the minimum preferred rental price corresponding to the set shared rental type; and step 103 is executed at the same time; Step 103: If the required rental type corresponding to the current consultant is the whole rental type, the rental type impact index corresponding to the current consultant is used as the housing type impact coefficient corresponding to the current consultant; if the required rental type corresponding to the current consultant is the shared rental type, the rental type impact index corresponding to the current consultant is used as the housing type impact coefficient corresponding to the current consultant, and step 106 is executed; Step 104: Obtain the preferred price range of the current consultant corresponding to the water and electricity charges, obtain the preferred rental price range corresponding to the current consultant, and analyze and obtain the housing type impact coefficient corresponding to the current consultant, specifically: obtain the preferred price range of the current consultant corresponding to the water and electricity charges, and extract the preferred maximum price of the current consultant corresponding to the water and electricity charges , tend to the minimum price ; Get the preferred rental price range corresponding to the current consultant, and extract the preferred maximum rental price corresponding to the current consultant , tend to have the lowest rental price , normalize the current consultant's maximum price, minimum price, maximum rental price, and minimum rental price for water and electricity charges and take their values, and according to the formula Calculate the housing type impact coefficient corresponding to the current consultant , a5, a6, a7, a8 are respectively represented as the set price proportional factors, and step 106 is executed at the same time; Step 105: Obtain the preferred area of ​​the shop corresponding to the current consultant , Product preference floor , normalize the shop preference area and shop preference floor corresponding to the current consultant and take their values, and according to the formula Calculate the house type impact coefficient corresponding to the current consultant , b1 and b2 are respectively represented as the set weight factors, and step 106 is executed; Step 106: Based on the required housing type corresponding to the current consultant, match it with the tendency parameters corresponding to each set required housing type to obtain the tendency parameters corresponding to the current consultant. At the same time, match the housing type influence coefficient corresponding to the current consultant with the housing type influence coefficient threshold corresponding to each tendency type in its corresponding tendency parameters to obtain the tendency type of the housing corresponding to the current consultant.

2. According to the big data-based information technology consulting management system of claim 1, it is characterized in that: The specific analysis process of analyzing the preliminary selected salespersons corresponding to the current consultant is as follows: 2-1: Extracting basic information corresponding to each house salesperson from the database, and extracting the gender and patience of each house salesperson from it, and extracting the gender and region of the current consultant from the basic information corresponding to the current consultant; wherein the patience of each house salesperson is obtained by: obtaining the historical evaluation corresponding to each house salesperson, the evaluation includes the patience score of the house salesperson, and calculating the average of each patience score of the historical evaluation corresponding to each house salesperson, to obtain the average patience score corresponding to each house salesperson, which is used as the patience of each house salesperson; 2-2: Match the gender of each house salesperson with the gender of the current consultant. If the gender of a house salesperson matches the gender of the current consultant, extract the gender influence factor of the set matching gender to obtain the gender influence factor of the house salesperson. Otherwise, extract the gender influence factor of the unmatched gender, thereby obtaining the gender influence factor of each house salesperson corresponding to the current consultant. ; Extract the location corresponding to the current consultant. If the location corresponding to the current consultant is local, extract the reference patience of the local salesperson. If the location corresponding to the current consultant is out of town, extract the reference patience of the out-of-town salesperson. Thus, the reference patience of the current consultant for house sales is obtained. ; According to the formula Calculate the basic information matching index of each house salesperson corresponding to the current consultant, It is represented as the matching index of the i-th house salesperson to the current consultant, i is the number corresponding to the house salesperson, It is represented by the patience corresponding to the i-th house salesperson, Expressed as the set weight factor, Expressed as a natural constant; 2-3: Compare the basic matching index of each house salesperson corresponding to the current consultant with the set basic matching index threshold. If the basic matching index of a house salesperson corresponding to the current consultant is greater than the set basic matching index threshold, the house salesperson is recorded as a designated salesperson, otherwise it is recorded as a eliminated salesperson, thereby obtaining each designated salesperson; 2-4: Extract the house sales type corresponding to each house salesperson from the database, and based on this, extract the house sales type corresponding to each designated salesperson, and match it with the tendency type of the house corresponding to the current consultant. If the house sales type corresponding to a designated salesperson matches the tendency type of the house corresponding to the current consultant successfully, the designated salesperson will be recorded as the preliminary salesperson, thereby obtaining the preliminary salespersons corresponding to the current consultant.

3. According to the information technology consulting management system based on big data as described in claim 1, it is characterized in that: The specific analysis steps for analyzing the second salesperson corresponding to the current consultant are as follows: 3-1: extracting the work information corresponding to each house salesperson from the database, and extracting the work information corresponding to each preliminary selected salesperson from the work information corresponding to each house salesperson; 3-2: Extract the number of remaining houses corresponding to each rental area of ​​each preliminary salesperson from the work information corresponding to each preliminary salesperson, and based on the preferred rental area corresponding to the current consultant, select the number of remaining houses in the preferred rental area of ​​each preliminary salesperson from the number of remaining houses in each rental area corresponding to each preliminary salesperson, and record it as the number of remaining houses in the matching area corresponding to each preliminary salesperson , j represents the number of each preliminary salesperson; 3-3: Extract the corresponding working years of each preliminary salesperson from the corresponding working information of each preliminary salesperson, and match the corresponding working years of each preliminary salesperson with the set reference total transaction volume corresponding to each working year, to obtain the corresponding reference total transaction volume of each preliminary salesperson At the same time, the historical total transaction volume corresponding to each preliminary salesperson is extracted from the work information corresponding to each preliminary salesperson ; 3-4: Normalize the number of remaining houses in the matching area of ​​each preliminary salesperson and take its value. At the same time, according to the formula Calculate the preferred index of each preliminary salesperson corresponding to the current consultant , b3 and b4 represent the weight factors corresponding to the set number of remaining houses and the total historical transaction volume respectively, and compare the preference index of each preliminary salesperson corresponding to the current consultant with the set reference preference index threshold. If the preference index of a preliminary salesperson corresponding to the current consultant is greater than the set reference preference index threshold, the preliminary salesperson is recorded as the second salesperson, thereby obtaining each second salesperson as the set of second salespersons corresponding to the current consultant.

4. The information technology consulting management system based on big data according to claim 3 is characterized in that: The specific analysis method for analyzing the preferred salespersons corresponding to the current consultant is as follows: The preferred indexes of the second salespersons corresponding to the current consultant are arranged in descending order, and a set number of salespersons are selected from the first one in turn as preferred salespersons, thereby obtaining the preferred salespersons corresponding to the current consultant.

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