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CN113902453A - A method and system for avoiding malicious competition - Google Patents

A method and system for avoiding malicious competition Download PDF

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CN113902453A
CN113902453A CN202111192542.XA CN202111192542A CN113902453A CN 113902453 A CN113902453 A CN 113902453A CN 202111192542 A CN202111192542 A CN 202111192542A CN 113902453 A CN113902453 A CN 113902453A
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customers
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CN113902453B (en
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李琦
赖艳
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Chongqing Ruiyun Technology Co ltd
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Chongqing Ruiyun Technology Co ltd
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Abstract

The invention provides a method and a system for avoiding malicious competition, wherein the method comprises the following steps: acquiring a target client to be distributed, wherein the target client carries target information; according to the target information, carrying out malicious competitor identification on the target client, and passing or intercepting the target client according to a malicious identification result; when the target client passes through the malicious competitor identification, carrying out real client identification on the target client according to the target information; and distributing the target client to the corresponding service personnel according to the real recognition result. The invention can screen out the situation that service personnel imitate customers, maintain good influence on ecological environment, relieve malicious competition caused by mechanism loopholes and reasonably arrange the reception mechanism of the service personnel.

Description

Method and system for avoiding malicious competition
Technical Field
The invention relates to the technical field of computers, in particular to a method and a system for avoiding malicious competition.
Background
Due to the rapid development of internet technology, the model of house sales has expanded from traditional face-to-face sales of service personnel to an online house marketing model. The prior 'consultation reception' in the on-line house property marketing mode has the defect of mechanism, for example, a salesman exhibition list which is attached to a house source and can be consulted by a client is used for displaying the sequencing and the real personal information of the salesman in a public way, and the prior house source or a building is used for the default salesman which is directly consulted by the client and also used for displaying the personal information in a public way. For example, if a current default salesperson a is consulted by a first customer, a has waited for the customer, the system will set the next salesperson B to become the customer for the current default waited for sale, and B will waited for the next second consultation.
Based on the mechanism, a salesperson can use a second mobile phone number and a micro signal of the salesperson to pretend to be a client to consult for current sales, and squeeze out original sales, so that the salesperson obtains a current default exhibition position and becomes a receptionist of the next real client, thereby increasing the probability of obtaining customers and being not beneficial to the benign operation of house property marketing.
Disclosure of Invention
Based on this, it is necessary to provide a method and system for avoiding malicious competition in view of the above technical problems.
A method of circumventing malicious competition, comprising the steps of: acquiring a target client to be distributed, wherein the target client carries target information; according to the target information, carrying out malicious competitor identification on the target client, and passing or intercepting the target client according to a malicious identification result; when the target client passes through the malicious competitor identification, carrying out real client identification on the target client according to the target information; and distributing the target client to the corresponding service personnel according to the real recognition result.
In one embodiment, before the obtaining the target customer to be distributed, the method further includes: and anonymizing the real information of the service staff at the default exhibition position on the network page, and replacing the display with a default head portrait and a random number.
In one embodiment, the identifying, according to the target information, a malicious competitor of the target customer, and passing or intercepting the target customer according to a malicious identification result specifically includes: the target information comprises a device identifier and an application software identifier, and the application software identifiers correspond to the target account numbers one by one; inquiring whether sales information exists under the target account according to the application software identification, and if the sales information exists, judging that the target account is a malicious competitor; if no sales information exists, carrying out real customer identification on the target account; or associating the device identifier with the application software identifier, querying a plurality of application software identifiers of the same device according to the device identifier, associating the plurality of application software identifiers with the device identifier, and if sales information exists in at least one of the plurality of application software identifiers, determining that the target account corresponding to the device identifier is a malicious competitor; if no sales information exists under all application software identifications, carrying out real customer identification on the target account; and marking a malicious competition identifier on the target account determined as the malicious competition, and hiding the consultation button for the target account carrying the malicious competition identifier.
In one embodiment, the performing, when the target customer is identified by the malicious competitor, real customer identification on the target customer according to the target information specifically includes: setting a buried point on a network page, and recording user behavior information, wherein the user behavior information comprises a page browsed by a user, clicking time and duration and times for browsing a building or a house source; analyzing and counting the user browsing duration according to the user behavior information, assigning a score to the user browsing duration, recording the score as a browsing duration score T, and calculating the browsing duration score according to whether a history transaction client exists or not; analyzing and counting the duration of the consultation problem according to the user behavior information, assigning a score to the duration of the consultation problem, recording the score as a consultation problem score Q, and calculating the consultation problem score according to whether a historical transaction client exists or not; analyzing and counting other behavior information according to the user behavior information, assigning scores to the other behavior information, recording the scores as other behavior scores O, and calculating the other behavior scores according to scoring rules; and calculating the pre-transaction rate of the user according to the browsing duration value T, the consultation question duration value Q and other behavior values O, and identifying the real user according to the pre-transaction rate.
In one embodiment, the analyzing and counting the browsing duration of the user according to the user behavior information, assigning a score to the browsing duration of the user, and recording the score as a browsing duration score T, and calculating the browsing duration score according to whether there is a history transaction client, specifically includes: when a history transaction client exists, the calculation rule of the browsing duration score is as follows: if the user browsing duration is more than (2+ n) than the average browsing duration of the submitted clients, then T ═ 45+ (n 0.1) n is more than or equal to 1; if the average browsing duration of the committed clients is less than the browsing duration of the user and is not more than 2, the average browsing duration of the committed clients is T-40; if the browsing duration of the user is equal to the average browsing duration of the committed clients, T is 35; if (1/2) the average browsing duration of the committed clients is less than or equal to the average browsing duration of the users and less than the average browsing duration of the committed clients, T is 30; if the user browsing time length is less than (1/2) the average time length of the finished clients, T is 20; when no history transaction client exists, the calculation rule of the browsing duration score is as follows:
if the total user duration/browsing times is more than 0 and less than or equal to 1 minute, T is 20; if the total browsing time length/browsing times of the user is less than or equal to 5 minutes and is less than 1 minute, T is equal to 30; if (the total browsing time length/browsing times of the user) is more than or equal to 5 minutes, then T is 40; if (total user browsing duration/browsing number) > 5 × n +1 min, T is 45+0.1 n.
In one embodiment, the analyzing and counting the duration of the consultation problem according to the user behavior information, assigning a score to the duration of the consultation problem, and recording as a consultation problem score Q, and calculating the consultation problem score according to whether a historical transaction client exists, specifically includes: when a historical transaction client exists, the calculation rule of the consultation problem score is as follows: if the total time length of the user consultation problems is more than (2+ n) the average consultation time length of the submitted clients, Q is 40+ (n is 0.1), and n is more than or equal to 1; if the total time of the user consultation problems is more than 2 and the average time of the consultation problems of the clients is up to 40; if the average consulting time of the committed clients is less than the total consulting time of the users and is not more than 2, and the average consulting time of the committed clients is less than 35; if the total time length of the user consultation problem is equal to the average consultation time length of the committed clients, Q is equal to 30; if (1/2) the average consulted duration of the committed clients is less than or equal to the user consulted duration less than the average consulted duration of the committed clients, Q is 25; if the user consultation duration is less than (1/2) and the average consultation duration of the submitted clients is reached, Q is 15; when no historical transaction client exists, the calculation rule of the consultation problem score is as follows: if the total time length of the user consultation problem/the consultation times are more than 0 and less than or equal to 1 minute, Q is 15; if the total time length of the user consultation problem/consultation times is less than or equal to 5 minutes and is less than 1 minute, Q is 25; if (the total duration of the user consultation questions/the consultation times) is more than or equal to 5 minutes, Q is 35; if (total duration of user consultation questions/number of consultation times) > 5 × n +1 min, Q is 40+0.1 n.
In one embodiment, the analyzing and counting other behavior information according to the user behavior information, assigning a score to the other behavior information, and recording as a score O of the other behavior, and calculating the score of the other behavior according to a scoring rule specifically includes: the other behaviors comprise collecting the building or house source, telephone contact and house watching, and the scores are respectively 2, 2 and 6; the scoring rule is as follows: o (collection + telephone contact + house watching) is less than or equal to 10.
In one embodiment, the calculating a pre-deal rate of the user according to the browsing duration score T, the consulting question duration score Q, and the other behavior scores O, and performing real user identification according to the pre-deal rate specifically includes: recording the user pre-transaction rate as S, wherein the calculation formula of the user pre-transaction rate is as follows: s ═ 100% of (T + Q + O); and identifying the real user according to a preset pre-transaction rate range.
In one embodiment, the allocating the target customer to the corresponding service person according to the real recognition result specifically includes: let W be the weight of the user consultation question, then W is Q/(T + Q + O) × 100%; when there is a history transaction client, let WAre all made ofConsulting a weighted average of the questions for the historical friendship client; mixing the W and the WAre all made ofComparing, if W is larger than or equal to WAre all made ofIf the default pre-transaction rate is 80%, the current target customer is distributed to the current service staff, and if W is less than WAre all made ofThen the next target customer is distributed to the next service personnel; and when no historical transaction client exists, recording the number of the current target clients to be distributed as n, and if the sum of the pre-transaction rates of the n clients does not reach 80%, ending the default reception by the target client and the current service staff to obtain the target information of the next target client.
A system to circumvent malicious competition, comprising: the target client acquisition module is used for acquiring a target client to be distributed, and the target client carries target information; the malicious competitor identification module is used for identifying malicious competitors for the target client according to the target information and a malicious identification result and passing or intercepting the target client; the real customer identification module is used for carrying out real customer identification on the target customer according to the target information when the target customer passes the identification of the malicious competitor; and the target customer distribution module is used for distributing the target customer to the corresponding service personnel according to the real identification result.
Compared with the prior art, the invention has the advantages and beneficial effects that: the invention can screen out the situation that service personnel imitate customers, maintain good influence on ecological environment, relieve malicious competition caused by mechanism loopholes and reasonably arrange the reception mechanism of the service personnel.
Drawings
FIG. 1 is a flow diagram of a method of avoiding malicious competition, according to an embodiment;
fig. 2 is a schematic structural diagram of a system for avoiding malicious competition in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings by way of specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In one embodiment, as shown in fig. 1, there is provided a method of avoiding malicious competition, comprising the steps of:
step S101, a target client to be distributed is obtained, and the target client carries target information.
Specifically, a target client to be distributed is obtained, the target client carries corresponding target information, and the client is identified according to the target information.
And S102, identifying malicious competitors for the target client according to the target information, and passing or intercepting the target client according to a malicious identification result.
Specifically, according to target information, identifying a malicious competitor for a target client, and if the target client is identified to be the malicious competitor, intercepting the target client; and if the target client is identified not to be the malicious competitor, carrying out real client identification on the target client, and further excluding the malicious competitor.
And step S103, when the target client passes through the identification of the malicious competitor, carrying out real client identification on the target client according to the target information.
Specifically, when the target client is identified by the malicious competitor, the real client identification is carried out on the target client according to the target information, so that the corresponding service personnel can be conveniently distributed to the target client.
And step S104, distributing the target client to the corresponding service personnel according to the real identification result.
Specifically, the target client is distributed to the corresponding service personnel according to the real recognition result.
In the embodiment, target information of a target client to be distributed is obtained, malicious competitor identification is performed on the target client according to the target information, the target client is passed or intercepted according to a malicious identification result, real client identification is performed on the target client according to the target information when the target client is identified by the malicious competitor, the target client is distributed to a corresponding service staff according to a real identification result, so that the situation that the service staff imitates the client is screened out, a good ecological environment is maintained, malicious competition caused by mechanism loopholes is relieved, and a reception mechanism of the service staff is reasonably arranged.
Before step S101, the method further includes: and anonymizing the real information of the service staff at the default exhibition position on the network page, and replacing the display with a default head portrait and a random number.
Specifically, before the target client is obtained, the information of the service staff at the exhibition position of the network page can be anonymized, the default head portrait and the random number are adopted to replace the exhibition, the target client can check the real information of the service staff to be waited after entering the consultation page, and the situation that the service staff falsely consults other service staff is effectively avoided.
Wherein, step S102 specifically includes: the target information comprises equipment identification and application software identification, and the application software identification corresponds to the target account one by one; inquiring whether sales information exists under a target account according to the application software identification, and if the sales information exists, judging the target account as a malicious competitor; if no sales information exists, real customer identification is carried out on the target account; or associating the device identifier with the application software identifiers, querying a plurality of application software identifiers of the same device according to the device identifier, associating the plurality of application software identifiers with the device identifier, and if sales information exists in at least one of the plurality of application software identifiers, determining that the target account corresponding to the device identifier is a malicious competitor; if no sales information exists under all application software identifications, carrying out real customer identification on the target account; and marking a malicious competition identifier on the target account determined as the malicious competition, and hiding the consultation button for the target account carrying the malicious competition identifier.
Specifically, the target information includes a device identifier and an application software identifier, the application software identifier corresponds to the target account one by one, and the device identifier and the application software identifier are obtained according to user authorization; according to the application software identification, inquiring whether sales information exists under a target account, and persisting user data information to a database, if sales information exists, judging that the target account is a malicious competitor, and if sales information does not exist, performing real customer identification on the target account; or the device identification is associated with the application software identification, a plurality of application software identifications of the same device are inquired according to the device identification, the plurality of application software identifications are associated with the device identification, if sales information exists in at least one of the plurality of application software identifications, a target account corresponding to the device identification is judged to be a malicious competitor, and if no sales information exists in all the application software identifications, real customer identification is carried out on the target account; and marking a malicious competition identifier on the target account determined to be in malicious competition, and hiding a consultation button for the target account carrying the malicious competition identifier, namely, the service personnel cannot consult the floor where the service personnel are located.
Wherein, step S103 specifically includes: setting a buried point on a network page, and recording user behavior information, wherein the user behavior information comprises a page browsed by a user, clicking time and duration and times for browsing a building or a house source; analyzing and counting the user browsing duration according to the user behavior information, assigning a score to the user browsing duration, recording the score as a browsing duration score T, and calculating the browsing duration score according to whether a history transaction client exists or not; analyzing and counting the duration of the consultation problem according to the user behavior information, assigning a score to the duration of the consultation problem, recording the score as a consultation problem score Q, and calculating the score of the consultation problem according to whether a historical transaction client exists or not; analyzing and counting other behavior information according to the user behavior information, assigning scores to the other behavior information, recording the scores as other behavior scores O, and calculating other behavior scores according to scoring rules; and calculating the pre-transaction rate of the user according to the browsing time length value T, the consultation question time length value Q and other behavior values O, and identifying the real user according to the pre-transaction rate.
Specifically, in one case, when the service person uses two mobile phones for malicious competition, the malicious competitors can be further excluded by real customer identification. Setting a buried point on a network page, and recording user behavior information, such as each page browsed by a user, each event clicked, and the duration and the number of times of browsing a building or a house source; analyzing and counting the browsing duration, the consultation problem duration and other behavior information of the user according to the behavior information of the user, assigning scores, respectively recording the scores as a browsing duration score T, a consultation problem score Q and other behavior scores O, calculating the pre-transaction rate of the user according to the three scores, and identifying the real user according to the pre-transaction rate.
When a history transaction client exists, the calculation rule of the browsing time length score is as follows: if the user browsing duration is more than (2+ n) than the average browsing duration of the submitted clients, then T ═ 45+ (n 0.1) n is more than or equal to 1; if the average browsing duration of the committed clients is less than the browsing duration of the user and is not more than 2, the average browsing duration of the committed clients is T-40; if the browsing duration of the user is equal to the average browsing duration of the committed clients, T is 35; if (1/2) the average browsing duration of the committed clients is less than or equal to the average browsing duration of the users and less than the average browsing duration of the committed clients, T is 30; if the user browsing time length is less than (1/2) the average time length of the finished clients, T is 20;
when no history transaction client exists, the calculation rule of the browsing duration score is as follows: if the total user duration/browsing times is more than 0 and less than or equal to 1 minute, T is 20; if the total browsing time length/browsing times of the user is less than or equal to 1 minute and less than or equal to 5 minutes, the T is 30, and if the total browsing time length/browsing times of the user is more than or equal to 5 minutes, the T is 40; if (total user browsing duration/browsing number) > 5 × n +1 min, T is 45+0.1 n.
Wherein, when the historical transaction client exists, the calculation rule of the consultation problem score is as follows: if the total time length of the user consultation problems is more than (2+ n) the average consultation time length of the submitted clients, Q is 40+ (n is 0.1), and n is more than or equal to 1; if the total time of the user consultation problems is more than 2 and the average time of the consultation problems of the clients is up to 40; if the average consulting time of the committed clients is less than the total consulting time of the users and is not more than 2, and the average consulting time of the committed clients is less than 35; if the total time length of the user consultation problem is equal to the average consultation time length of the committed clients, Q is equal to 30; if (1/2) the average consulted duration of the committed clients is less than or equal to the user consulted duration less than the average consulted duration of the committed clients, Q is 25; if the user consultation duration is less than (1/2) and the average consultation duration of the submitted clients is reached, Q is 15;
when no historical transaction client exists, the calculation rule of the consultation problem score is as follows: if the total time length of the user consultation problem/the consultation times are more than 0 and less than or equal to 1 minute, Q is 15; if the total time length of the user consultation problem/consultation times is less than or equal to 5 minutes and is less than 1 minute, Q is 25; if (the total duration of the user consultation questions/the consultation times) is more than or equal to 5 minutes, Q is 35; if (total duration of user consultation questions/number of consultation times) > 5 × n +1 min, Q is 40+0.1 n.
Wherein, other behaviors comprise collecting the building or house source, telephone contact and house watching, and the scores are respectively 2, 2 and 6; the scoring rule is as follows: o (collection + telephone contact + house watching) is less than or equal to 10.
Recording the user pre-transaction rate as S, wherein the calculation formula of the user pre-transaction rate is as follows:
S＝(T+Q+O)*100％；
and identifying the real user according to a preset pre-transaction rate range.
Specifically, for example, a pre-maturity of less than 40% is considered as an untrusted user, for which the consultation button is hidden; the pre-transaction rate is 40% -60%, the user is determined as an in-doubt user, and a consultation button is opened but the real information of the reception service staff is hidden; the pre-transaction rate exceeds 60%, the client is determined as a real client, and a consultation button and real information are opened; the pre-maturity of over 80% is considered as an important client to which a consultation button and real information are opened.
Wherein, step S104 specifically includes: let W be the weight of the user consultation question, then
W＝Q/(T+Q+O)*100％；
When there is a history transaction client, let WAre all made ofConsulting a weighted average of the questions for the historical friendship client; mixing W with WAre all made ofComparing, if W is larger than or equal to WAre all made ofIf the default pre-transaction rate is 80%, the target customer is allocated to the current service personnel, and if W is less than WAre all made ofThen the target customer is distributed to the next service personnel;
and when no historical transaction client exists, recording the number of the current target clients to be distributed as n, and if the sum of the pre-transaction rates of the n clients does not reach 80%, ending the default reception by the target client and the current service staff to obtain the target information of the next target client.
Specifically, when the historical transaction client exists, the weight of the user consultation problem is compared with the average value of the weights of the consultation problems of the historical transaction client, and W is more than or equal to WAre all made ofWhen the pre-transaction rate of the current target customer is up to 80%; at W < WAre all made ofThen, the next target customer is allocated to the next service staff; when no historical transaction client exists, the number of the current target clients to be distributed is recorded as n, if the sum of the pre-transaction rates of the n clients does not reach 80%, the current target client and the current service personnel finish the default reception to obtain the target information of the next target client, and the next target client is identified with the real client through malicious competitionAfter identification, the server is distributed to the next service personnel for reception.
As shown in fig. 2, there is provided a system 20 for avoiding malicious competition, comprising: a target client acquisition module 21, a malicious competitor identification module 22, a real client identification module 23 and a target client distribution module 24, wherein:
a target client obtaining module 21, configured to obtain a target client to be allocated, where the target client carries target information;
the malicious competitor identification module 22 is used for identifying malicious competitors for the target client according to the target information and the malicious identification result, and passing or intercepting the target client;
the real client identification module 23 is configured to perform real client identification on the target client according to the target information when the target client passes through the malicious competitor identification;
and the target client distribution module 24 distributes the target client to the corresponding service personnel according to the real identification result.
In one embodiment, the malicious competitor identification module 22 is specifically configured to: the target information comprises equipment identification and application software identification, and the application software identification corresponds to the target account one by one; inquiring whether sales information exists under a target account according to the application software identification, and if the sales information exists, judging the target account as a malicious competitor; if no sales information exists, real customer identification is carried out on the target account; or associating the device identifier with the application software identifiers, querying a plurality of application software identifiers of the same device according to the device identifier, associating the plurality of application software identifiers with the device identifier, and if sales information exists in at least one of the plurality of application software identifiers, determining that the target account corresponding to the device identifier is a malicious competitor; if no sales information exists under all application software identifications, carrying out real customer identification on the target account; and marking a malicious competition identifier on the target account determined as the malicious competition, and hiding the consultation button for the target account carrying the malicious competition identifier.
In one embodiment, the real client identification module 23 is specifically configured to: setting a buried point on a network page, and recording user behavior information, wherein the user behavior information comprises a page browsed by a user, clicking time and duration and times for browsing a building or a house source; analyzing and counting the user browsing duration according to the user behavior information, assigning a score to the user browsing duration, recording the score as a browsing duration score T, and calculating the browsing duration score according to whether a history transaction client exists or not; analyzing and counting the duration of the consultation problem according to the user behavior information, assigning a score to the duration of the consultation problem, recording the score as a consultation problem score Q, and calculating the score of the consultation problem according to whether a historical transaction client exists or not; analyzing and counting other behavior information according to the user behavior information, assigning scores to the other behavior information, recording the scores as other behavior scores O, and calculating other behavior scores according to scoring rules; and calculating the pre-transaction rate of the user according to the browsing time length value T, the consultation question time length value Q and other behavior values O, and identifying the real user according to the pre-transaction rate.
In one embodiment, the target customer allocation module 24 is specifically configured to: let W be the weight of the user consultation question, then W is Q/(T + Q + O) × 100%; when there is a history transaction client, let WAre all made ofConsulting a weighted average of the questions for the historical friendship client; mixing W with WAre all made ofComparing, if W is larger than or equal to WAre all made ofIf the default pre-transaction rate is 80%, distributing the current target customer to the current service personnel, and if W is less than WAre all made ofThen the next target customer is distributed to the next service personnel; and when no historical transaction client exists, recording the number of the current target clients to be distributed as n, and if the sum of the pre-transaction rates of the n clients does not reach 80%, ending the default reception by the target client and the current service staff to obtain the target information of the next target client.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented in program code executable by a computing device, such that they may be stored on a computer storage medium (ROM/RAM, magnetic disks, optical disks) and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (10)

1.一种规避恶意竞争的方法，其特征在于，包括以下步骤：1. a method for evading malicious competition, is characterized in that, comprises the following steps: 获取待分配目标客户，所述目标客户携带有目标信息；Obtaining target customers to be allocated, and the target customers carry target information; 根据所述目标信息，对所述目标客户进行恶意竞争者识别，根据恶意识别结果对目标客户进行通过或拦截；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; 在所述目标客户通过所述恶意竞争者识别时，则根据所述目标信息对所述目标客户进行真实客户识别；When the target customer is identified by the malicious competitor, the target customer is identified as a real customer according to the target information; 根据所述真实识别结果，将所述目标客户分配到对应的服务人员处。According to the real identification result, the target customer is assigned to the corresponding service personnel. 2.根据权利要求1所述的一种规避恶意竞争的方法，其特征在于，在所述获取待分配目标客户之前，还包括：2. The method for avoiding malicious competition according to claim 1, characterized in that, before said acquiring target customers to be allocated, further comprising: 将网络页面上默认展位处的服务人员真实信息匿名化，采用默认头像与随机编号代替展示。Anonymize the real information of the service personnel at the default booth on the web page, and use the default avatar and random number instead of display. 3.根据权利要求1所述的一种规避恶意竞争的方法，其特征在于，所述根据所述目标信息，根据恶意识别结果对所述目标客户进行恶意竞争者识别，对目标客户进行通过或拦截，具体包括：3. The method for evading malicious competition according to claim 1, wherein, according to the target information, according to the malicious identification result, the target customer is identified as a malicious competitor, and the target customer is identified by passing or Interception, including: 所述目标信息中包括有设备标识和应用软件标识，所述应用软件标识与目标账号一一对应；The target information includes a device identification and an application software identification, and the application software identification is in one-to-one correspondence with the target account; 根据所述应用软件标识，查询所述目标账号下是否存在销售信息，若存在销售信息，则判定所述目标账号为恶意竞争者；若不存在销售信息，则对所述目标账号进行真实客户识别；According to the application software identifier, query whether there is sales information under the target account, if there is sales information, then determine that the target account is a malicious competitor; if there is no sales information, then identify the target account as a real customer ; 或将所述设备标识与所述应用软件标识进行关联，根据所述设备标识查询相同设备的多个应用软件标识，将所述多个应用软件标识与所述设备标识进行关联，若多个应用软件标识中至少一个应用软件标识下存在销售信息，则判定所述设备标识对应的目标账号为恶意竞争者；若所有应用软件标识下均不存在销售信息，则对所述目标账号进行真实客户识别；Or associate the device identification with the application software identification, query multiple application software identifications of the same device according to the device identification, and associate the multiple application software identifications with the device identification. If there is sales information under at least one application software identification in the software identification, it is determined that the target account corresponding to the device identification is a malicious competitor; if there is no sales information under all application software identifications, the target account is identified as a real customer ; 对判定为恶意竞争的目标账号打上恶意竞争标识，对携带有恶意竞争标识的目标账号隐藏咨询按钮。Mark the target account determined to be malicious competition with the malicious competition logo, and hide the consultation button for the target account with the malicious competition logo. 4.根据权利要求1所述的一种规避恶意竞争的方法，其特征在于，所述在所述目标客户通过所述恶意竞争者识别时，根据所述目标信息对所述目标客户进行真实客户识别，具体包括：4 . The method for avoiding malicious competition according to claim 1 , wherein, when the target customer is identified by the malicious competitor, conducting a real customer evaluation on the target customer according to the target information. 5 . Identify, specifically: 在网络页面上设置埋点，记录用户行为信息，所述用户行为信息包括用户浏览的页面、点击的时间和浏览楼盘或房源的时长与次数；Setting buried points on the web page, and recording user behavior information, the user behavior information includes the pages browsed by the user, the time of the click, and the duration and times of browsing real estate or housing; 根据所述用户行为信息，分析统计出用户浏览时长，并对用户浏览时长赋值得分，记为浏览时长分值T，根据是否存在历史成交客户，计算所述浏览时长分值；According to the user behavior information, analyze and count the user's browsing time, assign a score to the user's browsing time, record it as the browsing time score T, and calculate the browsing time score according to whether there are historical transaction customers; 根据所述用户行为信息，分析统计出咨询问题时长，并对咨询问题时长赋值得分，记为咨询问题分值Q，根据是否存在历史成交客户，计算所述咨询问题分值；According to the user behavior information, the duration of the consultation question is analyzed and counted, and a score is assigned to the duration of the consultation question, which is recorded as the consultation question score Q, and the consultation question score is calculated according to whether there are historical transaction customers; 根据所述用户行为信息，分析统计出其他行为信息，并对其他行为信息赋值得分，记为其他行为分值O，根据计分规则计算所述其他行为分值；According to the user behavior information, analyze and count other behavior information, assign a score to the other behavior information, record it as the other behavior score 0, and calculate the other behavior score according to the scoring rule; 根据所述浏览时长分值T、咨询问题时长分值Q和其他行为分值O，计算用户预成交率，根据所述预成交率进行真实用户识别。According to the browsing duration score T, the consultation question duration score Q and other behavior scores O, the user pre-transaction rate is calculated, and the real user is identified according to the pre-transaction rate. 5.根据权利要求4所述的一种规避恶意竞争的方法，其特征在于，所述根据所述用户行为信息，分析统计出用户浏览时长，并对用户浏览时长赋值得分，记为浏览时长分值T，根据是否存在历史成交客户，计算所述浏览时长分值，具体包括：5. a kind of method for avoiding malicious competition according to claim 4, is characterized in that, described according to described user behavior information, analyze and count out user browsing duration, assign score to user browsing duration, be recorded as browsing duration score The value T, according to whether there is a historical transaction customer, calculate the browsing time score, which specifically includes: 在存在历史成交客户时，所述浏览时长分值的计算规则为：When there are historical transaction customers, the calculation rule for the browsing time score value is: 若用户浏览时长用户浏览时长＞(2+n)*已成交客户平均浏览时长，则T＝45+(n*0.1)n≥1；If the user browsing time User browsing time > (2+n)*the average browsing time of customers who have made transactions, then T=45+(n*0.1)n≥1; 若已成交客户平均浏览时长＜用户浏览时长≤2*已成交客户平均浏览时长，则T＝40；If the average browsing time of the customers who have made a deal < the browsing time of the user≤2*the average browsing time of the customers who have made a deal, then T=40; 若用户浏览时长＝已成交客户平均浏览时长，则T＝35；If the user browsing time = the average browsing time of the customers who have made a deal, then T=35; 若(1/2)*已成交客户平均浏览时长≤用户浏览时长＜已成交客户平均浏览时长，则T＝30；If (1/2)*the average browsing time of customers who have made a deal≤users’ browsing time＜the average browsing time of customers who have made a deal, then T=30; 若用户浏览时长＜(1/2)*已成交客户平均时长，则T＝20；If the user browsing time <(1/2)*the average time of customers who have made transactions, then T=20; 在不存在历史成交客户时，所述浏览时长分值的计算规则为：When there is no historical transaction customer, the calculation rule for the browsing duration score is: 若0＜用户总时长/浏览次数≤1分钟，则T＝20；If 0 < total user duration/number of browsing times ≤ 1 minute, then T=20; 若1分钟＜用户浏览总时长/浏览次数≤5分钟，则T＝30；If 1 minute < total user browsing time/number of browsing times ≤ 5 minutes, then T=30; 若(用户浏览总时长/浏览次数)≥5分钟，则T＝40；If (the total browsing time of the user/the number of browsing times) ≥ 5 minutes, then T=40; 若(用户浏览总时长/浏览次数)＞5*(n+1)min，则T＝45+0.1n。If (the total browsing time of the user/the number of browsing times)>5*(n+1)min, then T=45+0.1n. 6.根据权利要求4所述的一种规避恶意竞争的方法，其特征在于，所述根据所述用户行为信息，分析统计出咨询问题时长，并对咨询问题时长赋值得分，记为咨询问题分值Q，根据是否存在历史成交客户，计算所述咨询问题分值，具体包括：6. a kind of method for avoiding malicious competition according to claim 4, is characterized in that, according to described user behavior information, analyze and count out consultation question duration, and assign score to consultation question duration, be recorded as consultation question score. The value Q, according to whether there are historical transaction customers, calculate the score of the consulting question, which specifically includes: 在存在历史成交客户时，所述咨询问题分值的计算规则为：When there are historical transaction customers, the calculation rule for the score of the consulting question is: 若用户咨询问题总时长＞(2+n)*已成交客户平均咨询时长，则Q＝40+(n*0.1)，n≥1；If the total time of user consultation questions > (2+n)*the average consultation time of customers who have completed transactions, then Q=40+(n*0.1), n≥1; 若用户咨询问题总时长＞2*已成交客户平均咨询问题时长，则Q＝40；If the total time of user consultation questions > 2 * the average consultation time of customers who have completed transactions, then Q=40; 若已成交客户平均咨询时长＜用户咨询问题总时长≤2*已成交客户平均咨询时长，则Q＝35；Q=35 if the average consultation time of the customers that have been dealt < the total time of user consultation questions ≤ 2 * the average consultation time of the customers that have been dealt; 若用户咨询问题总时长＝已成交客户平均咨询时长，则Q＝30；If the total time of user consultation questions = the average consultation time of customers who have completed transactions, then Q=30; 若(1/2)*已成交客户平均咨询时长≤用户咨询时长＜已成交客户平均咨询时长，则Q＝25；Q=25 if (1/2)*the average consultation time of the customers that have been dealt ≤ user consultation time＜the average consultation time of the customers that have been dealt; 若用户咨询时长＜(1/2)*已成交客户平均咨询时长，则Q＝15；If the user consultation time <(1/2)*the average consultation time of the customers who have completed the transaction, then Q=15; 在不存在历史成交客户时，所述咨询问题分值的计算规则为：When there is no historical transaction customer, the calculation rule for the score of the consulting question is: 若0＜用户咨询问题总时长/咨询次数≤1分钟，则Q＝15；If 0 < the total duration of user consultation questions/number of consultations ≤ 1 minute, then Q=15; 若1分钟＜用户咨询问题总时长/咨询次数≤5分钟，则Q＝25；If 1 minute < total duration of user consultation/number of consultations ≤ 5 minutes, then Q=25; 若(用户咨询问题总时长/咨询次数)≥5分钟，则Q＝35；If (total duration of user consultation/number of consultations) ≥ 5 minutes, then Q=35; 若(用户咨询问题总时长/咨询次数)＞5*(n+1)min，则Q＝40+0.1n。If (the total duration of user consultation questions/number of consultations)>5*(n+1)min, then Q=40+0.1n. 7.根据权利要求4所述的一种规避恶意竞争的方法，其特征在于，所述根据所述用户行为信息，分析统计出其他行为信息，并对其他行为信息赋值得分，记为其他行为分值O，根据计分规则计算所述其他行为分值，具体包括：7. a kind of method for evading malicious competition according to claim 4, is characterized in that, described according to described user behavior information, analyze and count out other behavior information, and assign score to other behavior information, be recorded as other behavior score. The value is O, and the other behavior scores are calculated according to the scoring rules, including: 所述其他行为包括收藏该楼盘或房源、电话联系和看房，分值分别为2分、2分和6分；The other behaviors described include collecting the real estate or house listing, contacting by phone and viewing the house, and the points are respectively 2 points, 2 points and 6 points; 所述计分规则为：O＝(收藏+电话联系+看房)≤10。The scoring rule is: O = (collection + telephone contact + viewing) ≤ 10. 8.根据权利要求7所述的一种规避恶意竞争的方法，其特征在于，所述根据所述浏览时长分值T、咨询问题时长分值Q和其他行为分值O，计算用户预成交率，根据所述预成交率进行真实用户识别，具体包括：8. a kind of method for evading malicious competition according to claim 7, is characterized in that, described according to described browsing duration score T, consultation question duration score Q and other behavior scores O, calculate user's pre-transaction rate , and identify real users according to the pre-transaction rate, which specifically includes: 将所述用户预成交率记为S，用户预成交率的计算公式为：The user pre-transaction rate is denoted as S, and the calculation formula of the user's pre-transaction rate is: S＝(T+Q+O)*100％；S=(T+Q+O)*100%; 根据预先设置的预成交率范围进行真实用户识别。Real user identification is carried out according to the preset pre-transaction rate range. 9.根据权利要求4所述的一种规避恶意竞争的方法，其特征在于，所述根据所述真实识别结果，将所述目标客户分配到对应的服务人员处，具体包括：9. The method for avoiding malicious competition according to claim 4, wherein, according to the real identification result, the target customer is allocated to the corresponding service personnel, which specifically includes: 令W为用户咨询问题的权重，则W＝Q/(T+Q+O)*100％；Let W be the weight of the user consultation question, then W=Q/(T+Q+O)*100%; 在存在历史成交客户时，令W均为历史成交客户咨询问题的权重平均值；When there are historical transaction customers, let W be the weighted average of the inquiries from historical transaction customers; 将所述W与W均进行比较，若W≥W均，则默认所述预成交率为80％，将当前目标客户分配到当前服务人员处，若W＜W均，则将下一目标客户分配到下一个服务人员处；Compare the W with the W average . If W ≥ W average , the default pre-transaction rate is 80%, and the current target customer will be assigned to the current service personnel. If W < W average , the next target customer will be assigned. assigned to the next service personnel; 在不存在历史成交客户时，将当前待分配目标客户数量记为n，若n位客户的预成交率之和未达到80％，则目标客户和当前服务人员结束默认接待，获取下一目标客户的目标信息。When there are no historical transaction customers, the current number of target customers to be allocated is recorded as n. If the sum of the pre-transaction rate of n customers does not reach 80%, the target customer and the current service staff will end the default reception and obtain the next target customer. target information. 10.一种规避恶意竞争的系统，其特征在于，包括：10. A system for avoiding malicious competition, comprising: 目标客户获取模块，用于获取待分配目标客户，所述目标客户携带有目标信息；a target customer acquisition module, used to acquire target customers to be allocated, and the target customers carry target information; 恶意竞争者识别模块，用于根据所述目标信息，根据恶意识别结果对所述目标客户进行恶意竞争者识别，对目标客户进行通过或拦截；A malicious competitor identification module, configured to identify malicious competitors to the target customer according to the target information and according to the malicious identification result, and to pass or intercept the target customer; 真实客户识别模块，用于在所述目标客户通过所述恶意竞争者识别时，则根据所述目标信息对所述目标客户进行真实客户识别；A real customer identification module, configured to identify the target customer as a real customer according to the target information when the target customer is identified by the malicious competitor; 目标客户分配模块，根据所述真实识别结果，将所述目标客户分配到对应的服务人员处。The target customer allocation module, according to the real identification result, allocates the target customer to the corresponding service personnel.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108734366A (en) * 2017-04-24 2018-11-02 北京京东尚科信息技术有限公司 User identification method and its system
CN108833453A (en) * 2017-04-26 2018-11-16 腾讯科技（深圳）有限公司 A kind of method and apparatus determined using account
US20190026752A1 (en) * 2017-07-20 2019-01-24 Dong Min Kim System for providing real property information online and system for providing safe transaction service using bigdata-based grade classification and market value prediction
CN111275487A (en) * 2020-01-18 2020-06-12 李琦 Client data acquisition method and device, server and storage medium
CN111292140A (en) * 2020-03-19 2020-06-16 重庆锐云科技有限公司 Online customer intelligent distribution method
CN112396504A (en) * 2021-01-21 2021-02-23 北京天通慧智科技有限公司 E-commerce order intercepting method and device and electronic equipment
CN112580952A (en) * 2020-12-09 2021-03-30 腾讯科技（深圳）有限公司 User behavior risk prediction method and device, electronic equipment and storage medium

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108734366A (en) * 2017-04-24 2018-11-02 北京京东尚科信息技术有限公司 User identification method and its system
CN108833453A (en) * 2017-04-26 2018-11-16 腾讯科技（深圳）有限公司 A kind of method and apparatus determined using account
US20190026752A1 (en) * 2017-07-20 2019-01-24 Dong Min Kim System for providing real property information online and system for providing safe transaction service using bigdata-based grade classification and market value prediction
CN111275487A (en) * 2020-01-18 2020-06-12 李琦 Client data acquisition method and device, server and storage medium
CN111292140A (en) * 2020-03-19 2020-06-16 重庆锐云科技有限公司 Online customer intelligent distribution method
CN112580952A (en) * 2020-12-09 2021-03-30 腾讯科技（深圳）有限公司 User behavior risk prediction method and device, electronic equipment and storage medium
CN112396504A (en) * 2021-01-21 2021-02-23 北京天通慧智科技有限公司 E-commerce order intercepting method and device and electronic equipment

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