A method and system for avoiding malicious competition

Abstract

The present invention provides a method and system for avoiding malicious competition, wherein the method comprises: obtaining a target customer to be assigned, the target customer carrying target information; identifying the target customer as a malicious competitor according to the target information, and passing or intercepting the target customer according to the malicious identification result; when the target customer passes the malicious competitor identification, identifying the target customer as a real customer according to the target information; and assigning the target customer to a corresponding service personnel according to the real identification result. The present invention can screen out situations where service personnel impersonate customers, maintain a good ecological environment, alleviate malicious competition caused by mechanism loopholes, and reasonably arrange the reception mechanism of service personnel.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for avoiding malicious competition. Background Art

[0002] Due to the rapid development of Internet technology, the model of house sales has expanded from traditional face-to-face sales by service personnel to online real estate marketing. The "consultation reception" link in the current online real estate marketing model has mechanism drawbacks. For example, the list of salesperson booths available for customer consultation attached to the house is a public display of the salesperson's ranking and real personal information, and the default salesperson for direct consultation by customers in the current house or property is also a public display of personal information. For example, if the current default salesperson A is consulted by the first customer and A receives the customer, the system will set the next salesperson B as the current default reception salesperson, and B will receive the next customer who consults.

[0003] Based on the above mechanism, sales personnel may use their second mobile phone number or WeChat account to impersonate a "customer" to inquire about the current sales, squeeze out the original sales, obtain the current default booth, and become the receptionist of the next real customer to increase their chances of acquiring customers, which is not conducive to the healthy operation of real estate marketing. Summary of the invention

[0004] Based on this, it is necessary to provide a method and system for avoiding malicious competition in response to the above technical problems.

[0005] A method for avoiding malicious competition includes the following steps: obtaining a target customer to be assigned, the target customer carrying target information; identifying the target customer as a malicious competitor based on the target information, and passing or intercepting the target customer based on the malicious identification result; when the target customer passes the malicious competitor identification, identifying the target customer as a real customer based on the target information; and assigning the target customer to a corresponding service personnel based on the real identification result.

[0006] In one of the embodiments, before obtaining the target customers to be assigned, the method further includes: anonymizing the real information of the service personnel at the default booth on the web page, and using a default avatar and a random number to display instead.

[0007] In one of the embodiments, the target customer is identified as a malicious competitor based on the target information, and the target customer is passed or intercepted based on the malicious identification result, specifically including: the target information includes a device identifier and an application software identifier, and the application software identifier corresponds to the target account one by one; based on the application software identifier, it is queried whether there is sales information under the target account, and if there is sales information, the target account is determined to be a malicious competitor; if there is no sales information, the target account is identified as a real customer; or the device identifier is associated with the application software identifier, and multiple application software identifiers of the same device are queried based on the device identifier, and the multiple application software identifiers are associated with the device identifier. If there is sales information under at least one of the multiple application software identifiers, the target account corresponding to the device identifier is determined to be a malicious competitor; if there is no sales information under all application software identifiers, the target account is identified as a real customer; the target account determined to be malicious competition is marked with a malicious competition identifier, and the consultation button is hidden for the target account carrying the malicious competition identifier.

[0008] In one of the embodiments, when the target customer is identified by the malicious competitor, the target customer is identified as a real customer based on the target information, specifically including: setting a embedding point on a web page to record user behavior information, wherein the user behavior information includes the pages browsed by the user, the time of clicks, and the duration and number of browsing of real estate or housing sources; analyzing and counting the user browsing time based on the user behavior information, and assigning a score to the user browsing time, recorded as a browsing time score T, and calculating the browsing time score based on whether there are historical transaction customers; analyzing and counting the consultation question time based on the user behavior information, and assigning a score to the consultation question time, recorded as a consultation question score Q, and calculating the consultation question score based on whether there are historical transaction customers; analyzing and counting other behavior information based on the user behavior information, and assigning a score to the other behavior information, recorded as an other behavior score O, and calculating the other behavior score according to the scoring rules; calculating the user pre-transaction rate based on the browsing time score T, the consultation question time score Q, and the other behavior score O, and identifying the real user based on the pre-transaction rate.

[0009] In one of the embodiments, the user browsing time is analyzed and counted according to the user behavior information, and a score is assigned to the user browsing time, which is recorded as a browsing time score T. The browsing time score is calculated according to whether there are historical transaction customers, specifically including: when there are historical transaction customers, the calculation rule of the browsing time score is: if the user browsing time user browsing time> (2+n)*average browsing time of transaction customers, then T=45+(n*0.1)n≥1; if the average browsing time of transaction customers<user browsing time≤2*average browsing time of transaction customers, then T=40; if the user browsing time=average browsing time of transaction customers, then T=35; if (1 / 2)*average browsing time of transaction customers≤user browsing time<average browsing time of transaction customers, then T=30; if the user browsing time<(1 / 2)*average browsing time of transaction customers, then T=20; when there are no historical transaction customers, the calculation rule of the browsing time score is:

[0010] If 0 < total user browsing time / number of views ≤ 1 minute, then T = 20; if 1 minute < total user browsing time / number of views ≤ 5 minutes, then T = 30; if (total user browsing time / number of views) ≥ 5 minutes, then T = 40; if (total user browsing time / number of views) > 5*(n+1)min, then T = 45+0.1n.

[0011] In one of the embodiments, the consultation time is analyzed and counted according to the user behavior information, and a score is assigned to the consultation time, which is recorded as the consultation score Q. The consultation score is calculated according to whether there are historical transaction customers, specifically including: when there are historical transaction customers, the calculation rule of the consultation score is: if the total consultation time of the user is greater than (2+n)*the average consultation time of the transaction customers, then Q=40+(n*0.1), n≥1; if the total consultation time of the user is greater than 2*the average consultation time of the transaction customers, then Q=40; if the average consultation time of the transaction customers is less than the total consultation time of the user ≤2*the average consultation time of the transaction customers, then Q=35; if the total consultation time of the user is less than The average consultation time of customers who have completed a transaction, then Q=30; if (1 / 2)*average consultation time of customers who have completed a transaction ≤ user consultation time < average consultation time of customers who have completed a transaction, then Q=25; if user consultation time <(1 / 2)*average consultation time of customers who have completed a transaction, then Q=15; when there is no historical customer who has completed a transaction, the calculation rule of the consultation question score is: if 0 < total user consultation time / number of consultations ≤1 minute, then Q=15; if 1 minute < total user consultation time / number of consultations ≤5 minutes, then Q=25; if (total user consultation time / number of consultations) ≥5 minutes, then Q=35; if (total user consultation time / number of consultations) >5*(n+1)min, then Q=40+0.1n.

[0012] In one of the embodiments, other behavior information is analyzed and counted based on the user behavior information, and a score is assigned to the other behavior information, which is recorded as an other behavior score O. The other behavior score is calculated according to a scoring rule, which specifically includes: the other behaviors include collecting the property or house, making phone calls, and viewing the house, with scores of 2 points, 2 points, and 6 points respectively; the scoring rule is: O = (collection + phone calls + viewing the house) ≤ 10.

[0013] In one of the embodiments, the user pre-transaction rate is calculated based on the browsing time score T, the consultation question time score Q and other behavior scores O, and real user identification is performed based on the pre-transaction rate, specifically including: recording the user pre-transaction rate as S, and the calculation formula of the user pre-transaction rate is: S = (T+Q+O)*100%; real user identification is performed based on a pre-set pre-transaction rate range.

[0014] In one embodiment, the target customer is assigned to a corresponding service personnel according to the real recognition result, specifically including: let W be the weight of the user's consultation question, then W = Q / (T+Q+O)*100%; when there is a historical transaction customer, let W 均 is the weighted average of the historical transaction customer consultation questions; W and W 均 Compare, if W ≥ W 均 , the pre-contract rate is assumed to be 80%, and the current target customer is assigned to the current service personnel. If W<W 均 , the next target customer will be assigned to the next service staff; when there is no historical transaction customer, the current number of target customers to be assigned is recorded as n. If the sum of the pre-transaction rates of n customers does not reach 80%, the target customer and the current service staff will end the default reception and obtain the target information of the next target customer.

[0015] A system for avoiding malicious competition includes: a target customer acquisition module, used to acquire a target customer to be assigned, the target customer carries target information; a malicious competitor identification module, used to identify the target customer as a malicious competitor according to the target information and a malicious identification result, and pass or intercept the target customer; a real customer identification module, used to identify the target customer as a real customer according to the target information when the target customer passes the malicious competitor identification; and a target customer allocation module, used to allocate the target customer to a corresponding service personnel according to the real identification result.

[0016] Compared with the prior art, the advantages and beneficial effects of the present invention are: the present invention can screen out situations where service personnel impersonate customers, maintain a good ecological environment, alleviate malicious competition caused by mechanism loopholes, and reasonably arrange the reception mechanism of service personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for avoiding malicious competition in one embodiment;

[0018] Figure 2 The figure is a schematic diagram of the structure of a system for avoiding malicious competition in one embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below through specific implementation methods in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] In one embodiment, Figure 1 As shown, a method for avoiding malicious competition is provided, comprising the following steps:

[0021] Step S101, obtaining a target customer to be assigned, where the target customer carries target information.

[0022] Specifically, firstly, the target customer to be assigned is obtained, the target customer carries corresponding target information, and the customer is identified according to the target information.

[0023] Step S102, based on the target information, malicious competitors are identified for the target customer, and the target customer is allowed or blocked based on the malicious identification result.

[0024] Specifically, based on the target information, the target customer is identified as a malicious competitor. If the target customer is identified as a malicious competitor, the target customer is intercepted; if the target customer is identified as not a malicious competitor, the target customer is identified as a real customer to further exclude malicious competitors.

[0025] Step S103, when the target customer is identified by the malicious competitor, the target customer is identified as a real customer according to the target information.

[0026] Specifically, when a target customer is identified through a malicious competitor, the target customer is identified as a real customer based on the target information, so as to facilitate the allocation of corresponding service personnel to the target customer.

[0027] Step S104: assign the target customer to the corresponding service personnel according to the real recognition result.

[0028] Specifically, the target customer is assigned to the corresponding service personnel according to the actual recognition result.

[0029] In this embodiment, the target information of the target customer to be assigned is obtained, and the target customer is identified as a malicious competitor based on the target information. The target customer is passed or intercepted based on the malicious identification result. When the target customer passes the malicious competitor identification, the target customer is identified as a real customer based on the target information, and the target customer is assigned to the corresponding service personnel based on the real identification result, thereby screening out situations where service personnel impersonate customers, maintaining a good ecological environment, alleviating malicious competition caused by mechanism loopholes, and reasonably arranging the reception mechanism of service personnel.

[0030] Before step S101, the method further includes: anonymizing the real information of the service personnel at the default booth on the web page, and using a default avatar and a random number to display them instead.

[0031] Specifically, before acquiring the target customer, the service staff information displayed on the web page can also be anonymized and displayed instead with a default avatar and a random number. The target customer can only view the real information of the receiving service staff after entering the consultation page, effectively avoiding the situation where the service staff impersonates the customer and maliciously consults other service staff.

[0032] Among them, step S102 specifically includes: the target information includes a device identifier and an application software identifier, and the application software identifier corresponds to the target account one by one; according to the application software identifier, query whether there is sales information under the target account, if there is sales information, the target account is determined to be a malicious competitor; if there is no sales information, the target account is identified as a real customer; or the device identifier is associated with the application software identifier, and multiple application software identifiers of the same device are queried according to the device identifier, and the multiple application software identifiers are associated with the device identifier. If there is sales information under at least one of the multiple application software identifiers, the target account corresponding to the device identifier is determined to be a malicious competitor; if there is no sales information under all application software identifiers, the target account is identified as a real customer; the target account determined to be malicious competition is marked with a malicious competition identifier, and the consultation button is hidden for the target account carrying the malicious competition identifier.

[0033] Specifically, the target information includes a device identifier and an application software identifier. The application software identifier corresponds to the target account one-to-one, and the device identifier and the application software identifier are obtained based on user authorization; based on the application software identifier, it is queried whether there is sales information under the target account, and the user data information is persisted in the database. If there is sales information, the target account is determined to be a malicious competitor. If there is no sales information, the target account is identified as a real customer; or the device identifier and the application software identifier are associated, and multiple application software identifiers of the same device are queried based on the device identifier, and the multiple application software identifiers are associated with the device identifier. If there is sales information under at least one of the multiple application software identifiers, the target account corresponding to the device identifier is determined to be a malicious competitor. If there is no sales information for all application software identifiers, the target account is identified as a real customer; a malicious competition identifier is marked on the target account determined to be a malicious competition, and the consultation button is hidden for the target account carrying the malicious competition identifier, that is, the service personnel cannot consult the property they are in.

[0034] Among them, step S103 specifically includes: setting a embedding point on the network page to record user behavior information, the user behavior information includes the pages browsed by the user, the time of clicking, and the duration and number of browsing of the property or house; analyzing and counting the user browsing time based on the user behavior information, and assigning a score to the user browsing time, recorded as the browsing time score T, and calculating the browsing time score based on whether there are historical transaction customers; analyzing and counting the consultation question time based on the user behavior information, and assigning a score to the consultation question time, recorded as the consultation question score Q, and calculating the consultation question score based on whether there are historical transaction customers; analyzing and counting other behavior information based on the user behavior information, and assigning a score to other behavior information, recorded as other behavior score O, and calculating other behavior scores according to the scoring rules; calculating the user pre-transaction rate based on the browsing time score T, the consultation question time score Q and the other behavior score O, and identifying real users based on the pre-transaction rate.

[0035] Specifically, in one case, when service personnel use two mobile phones to engage in malicious competition, malicious competitors can be further excluded through real customer identification. Set up embedding points on the web page to record user behavior information, such as each page the user browses, each event clicked, and the duration and number of browsing of real estate or housing sources; based on user behavior information, analyze and count the user's browsing time, consultation time and other behavior information, and assign scores, which are recorded as browsing time score T, consultation question score Q and other behavior score O respectively, and calculate the user's pre-transaction rate based on the above three scores, and identify real users based on the pre-transaction rate.

[0036] Among them, when there are historical transaction customers, the calculation rule of browsing time score is: if user browsing time > (2 + n) * average browsing time of transaction customers, then T = 45 + (n * 0.1) n ≥ 1; if the average browsing time of transaction customers < user browsing time ≤ 2 * average browsing time of transaction customers, then T = 40; if user browsing time = average browsing time of transaction customers, then T = 35; if (1 / 2) * average browsing time of transaction customers ≤ user browsing time < average browsing time of transaction customers, then T = 30; if user browsing time < (1 / 2) * average browsing time of transaction customers, then T = 20;

[0037] When there are no historical transaction customers, the calculation rule of the browsing time score is: if 0 < total browsing time of the user / number of browsing times ≤ 1 minute, then T = 20; if 1 minute < total browsing time of the user / number of browsing times ≤ 5 minutes, then T = 30; if (total browsing time of the user / number of browsing times) ≥ 5 minutes, then T = 40; if (total browsing time of the user / number of browsing times) > 5*(n+1)min, then T = 45+0.1n.

[0038] Among them, when there are historical transaction customers, the calculation rule of the consultation question score is: if the total time of user consultation questions> (2+n)*average consultation time of transaction customers, then Q=40+(n*0.1), n≥1; if the total time of user consultation questions>2*average consultation time of transaction customers, then Q=40; if the average consultation time of transaction customers<total time of user consultation questions≤2*average consultation time of transaction customers, then Q=35; if the total time of user consultation questions=average consultation time of transaction customers, then Q=30; if (1 / 2)*average consultation time of transaction customers≤user consultation time<average consultation time of transaction customers, then Q=25; if user consultation time<(1 / 2)*average consultation time of transaction customers, then Q=15;

[0039] When there are no customers with historical transactions, the calculation rules for the consultation question score are: if 0 < total duration of user consultation questions / number of consultations ≤ 1 minute, then Q = 15; if 1 minute < total duration of user consultation questions / number of consultations ≤ 5 minutes, then Q = 25; if (total duration of user consultation questions / number of consultations) ≥ 5 minutes, then Q = 35; if (total duration of user consultation questions / number of consultations) > 5*(n+1)min, then Q = 40+0.1n.

[0040] Among them, other behaviors include collecting the property or listing, making phone calls and viewing the house, with scores of 2 points, 2 points and 6 points respectively; the scoring rule is: O=(collection+phone calls+viewing the house)≤10.

[0041] The user pre-transaction rate is recorded as S, and the calculation formula of the user pre-transaction rate is:

[0042] S = (T + Q + O) * 100%;

[0043] Identify real users based on the pre-set pre-conversion rate range.

[0044] Specifically, for example, users with a pre-conversion rate lower than 40% are identified as untrustworthy users, and the consultation button is hidden from them; users with a pre-conversion rate between 40% and 60% are identified as suspicious users, and the consultation button is opened to them, but the real information of the reception service staff is hidden; users with a pre-conversion rate exceeding 60% are identified as real customers, and the consultation button and real information are opened to them; users with a pre-conversion rate exceeding 80% are identified as important customers, and the consultation button and real information are opened to them.

[0045] Wherein, step S104 specifically includes: let W be the weight of the user's consultation question, then

[0046] W = Q / (T + Q + O) * 100%;

[0047] When there are historical transaction customers, let W 均 is the weighted average of the historical transaction customer consultation questions; W and W 均 Compare, if W ≥ W 均 , the default pre-contract rate is 80%, and the target customer is assigned to the current service personnel. If W<W 均 , the target customer will be assigned to the next service staff;

[0048] When there are no historical transaction customers, the current number of target customers to be assigned is recorded as n. If the sum of the pre-transaction rates of n customers does not reach 80%, the target customer and the current service staff end the default reception and obtain the target information of the next target customer.

[0049] Specifically, when there are historical transaction customers, the weight of the user's consultation question is compared with the average weight of the historical transaction customer's consultation question. 均 When W<W 均 , the next target customer is assigned to the next service staff; when there is no historical transaction customer, the current number of target customers to be assigned is recorded as n. If the sum of the pre-transaction rates of n customers does not reach 80%, the current target customer and the current service staff end the default reception, obtain the target information of the next target customer, and the next target customer is assigned to the next service staff for reception after malicious competition identification and real customer identification.

[0050] like Figure 2 As shown, a system 20 for avoiding malicious competition is provided, comprising: a target customer acquisition module 21, a malicious competitor identification module 22, a real customer identification module 23 and a target customer allocation module 24, wherein:

[0051] The target customer acquisition module 21 is used to acquire the target customer to be assigned, and the target customer carries the target information;

[0052] Malicious competitor identification module 22, used to identify malicious competitors of target customers according to the target information and malicious identification results, and pass or intercept the target customers;

[0053] A real customer identification module 23, used for performing real customer identification on the target customer according to the target information when the target customer is identified by the malicious competitor;

[0054] The target customer allocation module 24 allocates the target customer to the corresponding service personnel according to the real recognition result.

[0055] In one embodiment, the malicious competitor identification module 22 is specifically used for: the target information includes a device identifier and an application software identifier, and the application software identifier corresponds to the target account one by one; according to the application software identifier, query whether there is sales information under the target account, if there is sales information, determine that the target account is a malicious competitor; if there is no sales information, perform real customer identification on the target account; or associate the device identifier with the application software identifier, query multiple application software identifiers of the same device according to the device identifier, and associate the multiple application software identifiers with the device identifier. If there is sales information under at least one of the multiple application software identifiers, determine that the target account corresponding to the device identifier is a malicious competitor; if there is no sales information under all application software identifiers, perform real customer identification on the target account; mark the target account determined to be malicious competition with a malicious competition identifier, and hide the consultation button for the target account carrying the malicious competition identifier.

[0056] In one embodiment, the real customer identification module 23 is specifically used to: set up embedding points on the network page to record user behavior information, the user behavior information includes the pages browsed by the user, the time of clicks, and the duration and number of browsing of real estate or housing sources; based on the user behavior information, analyze and count the user browsing time, and assign a score to the user browsing time, recorded as the browsing time score T, and calculate the browsing time score according to whether there are historical transaction customers; based on the user behavior information, analyze and count the consultation question time, and assign a score to the consultation question time, recorded as the consultation question score Q, and calculate the consultation question score according to whether there are historical transaction customers; based on the user behavior information, analyze and count other behavior information, and assign a score to other behavior information, recorded as other behavior score O, and calculate other behavior scores according to the scoring rules; calculate the user pre-transaction rate based on the browsing time score T, the consultation question time score Q and the other behavior score O, and identify the real users based on the pre-transaction rate.

[0057] In one embodiment, the target customer allocation module 24 is specifically used to: let W be the weight of the user's consultation question, then W = Q / (T+Q+O)*100%; when there are historical transaction customers, let W 均 is the weighted average of the historical transaction customer consultation questions; W and W 均 Compare, if W ≥ W 均 , the default pre-contract rate is 80%, and the current target customer is assigned to the current service personnel. If W<W 均 , the next target customer will be assigned to the next service staff; when there is no historical transaction customer, the current number of target customers to be assigned is recorded as n. If the sum of the pre-transaction rates of n customers does not reach 80%, the target customer and the current service staff will end the default reception and obtain the target information of the next target customer.

[0058] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0059] Obviously, those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a computer storage medium (ROM / RAM, magnetic disk, optical disk) and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Therefore, the present invention is not limited to any specific combination of hardware and software.

[0060] The above contents are further detailed descriptions of the present invention in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for avoiding malicious competition, characterized in that: The following steps are involved: Acquire a target customer to be assigned, wherein the target customer carries target information; According to the target information, the target customer is identified as a malicious competitor, and the target customer is passed or intercepted according to the malicious identification result, including: the target information includes a device identifier and an application software identifier, and the application software identifier corresponds to the target account one by one; according to the application software identifier, it is queried whether there is sales information under the target account, and if there is sales information, the target account is determined to be a malicious competitor; if there is no sales information, the target account is identified as a real customer; or the device identifier is associated with the application software identifier, and multiple application software identifiers of the same device are queried according to the device identifier, and the multiple application software identifiers are associated with the device identifier. If there is sales information under at least one of the multiple application software identifiers, the target account corresponding to the device identifier is determined to be a malicious competitor; if there is no sales information under all application software identifiers, the target account is identified as a real customer; the target account determined to be malicious competition is marked with a malicious competition identifier, and the consultation button is hidden for the target account carrying the malicious competition identifier; When the target customer is identified by the malicious competitor, the target customer is identified as a real customer based on the target information, including: setting a embedding point on the web page to record user behavior information, the user behavior information including the pages browsed by the user, the time of clicking, and the duration and number of browsing of real estate or housing sources; analyzing and counting the user browsing time based on the user behavior information, and assigning a score to the user browsing time, recorded as a browsing time score T, and calculating the browsing time score based on whether there are historical transaction customers; analyzing and counting the consultation question time based on the user behavior information, and assigning a score to the consultation question time, recorded as a consultation question score Q, and calculating the consultation question score based on whether there are historical transaction customers; analyzing and counting other behavior information based on the user behavior information, and assigning a score to other behavior information, recorded as other behavior score O, and calculating the other behavior score according to the scoring rules; calculating the user pre-transaction rate based on the browsing time score T, the consultation question score Q, and the other behavior score O, and identifying the real user based on the pre-transaction rate; According to the real customer identification result, the target customer is assigned to the corresponding service personnel.

2. A method for avoiding malicious competition according to claim 1, characterized in that: Before obtaining the target customers to be allocated, the method further includes: The real information of the service staff at the default booth on the web page is anonymized and replaced with a default avatar and random number.

3. A method for avoiding malicious competition according to claim 1, characterized in that: The method of analyzing and calculating the browsing time of users according to the user behavior information and assigning a score to the browsing time of users, which is recorded as the browsing time score T, and calculating the browsing time score according to whether there is a historical transaction customer, specifically includes: When there are historical transaction customers, the calculation rule of the browsing time score is: If the user browsing time > (2 + n) * average browsing time of completed customers, then T = 45 + (n * 0.1), n ​​≥ 1; If the average browsing time of completed customers is less than the user browsing time ≤ 2*the average browsing time of completed customers, then T = 40; If the user browsing time = the average browsing time of completed customers, then T = 35; If (1 / 2)*average browsing time of completed customers ≤ user browsing time < average browsing time of completed customers, then T = 30; If the user's browsing time is less than (1 / 2)*the average browsing time of completed customers, then T=20; When there is no historical transaction customer, the calculation rule of the browsing time score is: If 0<total browsing time of user / number of browsing times≤1 minute, then T=20; If 1 minute < total browsing time / number of browsing times ≤ 5 minutes, then T = 30; If 5 minutes < total browsing time / number of browsing times ≤ 5*(n+1) minutes, then T = 40; If (total browsing time of the user / number of browsing times)>5*(n+1) minutes, then T=45+0.1n.

4. A method for avoiding malicious competition according to claim 1, characterized in that: According to the user behavior information, the consultation time is analyzed and counted, and a score is assigned to the consultation time, which is recorded as the consultation score Q. According to whether there is a historical transaction customer, the consultation score is calculated, which specifically includes: When there are historical transaction customers, the calculation rules for the consulting question score are as follows: If the total time spent on user consultation is greater than (2+n)*average time spent on consultation by successful customers, then Q=40+(n*0.1), n≥1; If 2*average consultation time of completed customers < total consultation time of users ≤ (2+n)*average consultation time of completed customers, then Q=40; If the average consultation time of completed customers is less than the total consultation time of users, ≤ 2*average consultation time of completed customers, then Q = 35; If the total time spent on user consultation = the average time spent on consultation by successful customers, then Q = 30; If (1 / 2)*average consultation time of completed customers ≤ user consultation time < average consultation time of completed customers, then Q = 25; If the user's consultation time is less than (1 / 2)*the average consultation time of completed customers, then Q=15; When there is no historical transaction customer, the calculation rule of the consulting question score is: If 0<total time of user consultation questions / number of consultations≤1 minute, then Q=15; If 1 minute < total time of user consultation questions / number of consultations ≤ 5 minutes, then Q = 25; If 5 minutes < total time of user consultation questions / number of consultations ≤ 5*(n+1) minutes, then Q = 35; If (total time spent on user consultation / number of consultations)>5*(n+1) minutes, then Q=40+0.1n.

5. The method for avoiding malicious competition according to claim 1, characterized in that: The analyzing and counting other behavior information according to the user behavior information, and assigning scores to other behavior information, recorded as other behavior score O, and calculating the other behavior score according to the scoring rules, specifically includes: The other actions include collecting the property or listing, making phone calls, and viewing the property, which are worth 2 points, 2 points, and 6 points respectively; The scoring rule is: O=(collection+telephone contact+house viewing)≤10.

6. A method for avoiding malicious competition according to claim 5, characterized in that: The user pre-transaction rate is calculated according to the browsing time score T, the consulting question score Q and other behavior scores O, and the real user identification is performed according to the pre-transaction rate, specifically including: The user pre-transaction rate is recorded as S, and the calculation formula of the user pre-transaction rate is: S = (T + Q + O) * 100%; Identify real users based on the pre-set pre-conversion rate range.

7. The method for avoiding malicious competition according to claim 1, characterized in that: The step of assigning the target customer to a corresponding service personnel according to the real customer identification result specifically includes: Let W be the weight of the user's inquiry question, then W = Q / (T+Q+O)*100%; When there are historical transaction customers, let W 均 The weighted average of the historical transaction customer consultation questions; The W and W 均 Compare, if W ≥ W 均 , the pre-contract rate is assumed to be 80%, and the current target customer is assigned to the current service personnel. If W<W 均 , then the next target customer will be assigned to the next service staff; When there are no historical transaction customers, the current number of target customers to be assigned is recorded as n. If the sum of the pre-transaction rates of n customers does not reach 80%, the target customer and the current service staff end the default reception and obtain the target information of the next target customer.

8. A system for avoiding malicious competition, characterized in that: include: A target customer acquisition module is used to acquire a target customer to be assigned, wherein the target customer carries target information; A malicious competitor identification module is used to identify the target customer as a malicious competitor according to the target information, and to pass or intercept the target customer according to the malicious identification result, including: the target information includes a device identifier and an application software identifier, and the application software identifier corresponds to the target account one by one; according to the application software identifier, query whether there is sales information under the target account, if there is sales information, determine that the target account is a malicious competitor; if there is no sales information, perform real customer identification on the target account; or associate the device identifier with the application software identifier, query multiple application software identifiers of the same device according to the device identifier, associate the multiple application software identifiers with the device identifier, if there is sales information under at least one of the multiple application software identifiers, determine that the target account corresponding to the device identifier is a malicious competitor; if there is no sales information under all application software identifiers, perform real customer identification on the target account; mark the target account determined to be malicious competition with a malicious competition identifier, and hide the consultation button for the target account carrying the malicious competition identifier; A real customer identification module is used to identify the target customer as a real customer according to the target information when the target customer is identified by the malicious competitor, including: setting a embedding point on a network page to record user behavior information, wherein the user behavior information includes the pages browsed by the user, the time of clicking, and the duration and number of browsing of real estate or housing sources; analyzing and counting the user browsing time according to the user behavior information, and assigning a score to the user browsing time, which is recorded as a browsing time score T, and calculating the browsing time score according to whether there are historical transaction customers; analyzing and counting the consultation question time according to the user behavior information, and assigning a score to the consultation question time, which is recorded as a consultation question score Q, and calculating the consultation question score according to whether there are historical transaction customers; analyzing and counting other behavior information according to the user behavior information, and assigning a score to other behavior information, which is recorded as other behavior score O, and calculating the other behavior score according to the scoring rules; calculating the user pre-transaction rate according to the browsing time score T, the consultation question score Q, and the other behavior score O, and identifying the real user according to the pre-transaction rate; The target customer allocation module allocates the target customer to the corresponding service personnel according to the real customer identification result.

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