CN113269916B - Guest prejudging analysis method and system based on face recognition - Google Patents
Guest prejudging analysis method and system based on face recognition Download PDFInfo
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- CN113269916B CN113269916B CN202110536467.8A CN202110536467A CN113269916B CN 113269916 B CN113269916 B CN 113269916B CN 202110536467 A CN202110536467 A CN 202110536467A CN 113269916 B CN113269916 B CN 113269916B
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/30—Individual registration on entry or exit not involving the use of a pass
- G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
- G07C9/37—Individual registration on entry or exit not involving the use of a pass in combination with an identity check using biometric data, e.g. fingerprints, iris scans or voice recognition
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/107—Static hand or arm
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
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Abstract
The invention discloses a guest prejudgment analysis method and system based on face recognition. It comprises the following steps: step 1: identifying and obtaining facial feature vectors of a plurality of persons entering an entrance of a unit in batch; step 2: judging whether strangers exist in the multiple persons or not according to the facial feature vectors of the multiple persons; and step 3: if a stranger exists, entering the step 4; and 4, step 4: judging whether an internal accompanying person exists or not, if so, acquiring the security level of the internal accompanying person from an internal accompanying person database according to the facial feature vector of the internal accompanying person, determining the security level of a stranger according to the security level of the internal accompanying person and the number of corresponding persons, and entering step 5; if no internal accompanying person exists, the process goes to step 6. The invention can automatically judge the security level of the visitor and allocate proper passing authority according to the identity and attitude of the internal accompanying person.
Description
Technical Field
The invention belongs to the technical field of security entrance guard identification, and particularly relates to a guest prejudgment analysis method and system based on face identification.
Background
The face recognition technology is widely applied to organs or enterprises and public institutions, face access control equipment is installed in each floor and each room, graded security management is achieved through the face access control equipment, and low security levels cannot enter areas, buildings and rooms with high security levels. However, institutions or enterprises often have visitors to communicate, visit or visit. Because the visitor does not belong to the internal staff and is not convenient to provide the face information in advance, the face access control system judges the visitor personnel as strangers and limits the passage of the visitor. Therefore, the guest prejudgment analysis system based on the face recognition can automatically judge the security level of the visitor and allocate proper passing permission according to the identity and attitude of the internal accompanying person.
The visitor visits for the first time, and generally enters a hall of an organization accompanied by an internal accompanying person. A face recognition device is required to be arranged in a hall, the security level of an internal accompanying person and the body language of the person visiting the visitor are utilized, the human body postures of the visitor and the internal accompanying person are automatically recognized by a face recognition algorithm, the security level of the visitor is automatically judged, then the face information of the visitor is automatically added into a face access control system, and the visitor can smoothly communicate, visit and observe in a unit. The method not only ensures the free passage of the guest, but also can limit the guest from entering a high security level area, and prevents information leakage.
Therefore, it is very necessary to design a new guest prediction analysis method and system based on face recognition.
Disclosure of Invention
The invention aims to solve the defects in the background technology and provides a guest prejudgment analysis method and system based on face recognition.
The technical scheme adopted by the invention is as follows: a guest prejudgment analysis method based on face recognition comprises the following steps:
step 1: identifying and obtaining facial feature vectors of a plurality of persons entering an entrance of a unit in batch;
step 2: judging whether strangers exist in the multiple persons or not according to the facial feature vectors of the multiple persons;
and step 3: if a stranger exists, entering the step 4;
and 4, step 4: judging whether an internal accompanying person exists or not, if so, acquiring the security level of the internal accompanying person from an internal accompanying person database according to the facial feature vector of the internal accompanying person, determining the security level of a stranger according to the security level of the internal accompanying person and the number of corresponding persons, and entering step 5; if no internal accompanying person exists, entering step 6;
and 5: adding the facial feature vector and the security level of the stranger to a database of the visitant group;
step 6: judging whether the working time is a working time period, and if so, early warning in real time; if not, real-time alarming is carried out.
Early warning is likely to occur, requiring constant attention and tracking. And (4) alarming: it is necessary for security personnel to go to the site immediately for treatment.
In the step 4: judging whether 2 or more internal accompanying persons with the highest security level exist, and if so, determining the security level of the stranger as the highest security level; if not, judging whether a handshake behavior of a stranger and an internal accompanying person exists or not.
In the step 4: if the handshake behavior of the stranger and the internal accompanying person exists, determining that the security level of the stranger is the same as that of the person with the highest security level in the internal accompanying person who shakes hands; if not, whether a stranger stands still or not is judged when the internal accompanying person speaks.
In the step 4: when an internal accompanying person speaks, if a stranger does not stand, determining that the security level of the stranger is equal to +1 of the highest security level of the internal accompanying person, and the highest security level cannot exceed the highest security level of the internal accompanying person; and if the stranger moves the position, acquiring the position of the stranger in the internal accompanying person.
In the step 4: if the strangers are located on two sides of the position, determining that the security level of the strangers is equal to that of the nearest internal accompanying person; if the position of the stranger is not on two sides or in the middle, determining that the security level of the stranger is equal to the maximum security level of the nearest internal accompanying person; if the stranger is in the middle (standing at the position C), the security level of the stranger is determined to be the same as the maximum security level of all the internal accompanying persons.
In the step 1, facial feature vectors of a plurality of persons entering an entrance of a unit within a preset time t are identified and acquired in batch.
The preset time t ranges from 0.3 to 3 seconds.
A guest pre-judging analysis system based on face recognition comprises
The face recognition module is arranged at the entrance of the unit and used for recognizing and acquiring facial feature vectors of a plurality of persons entering the entrance of the unit in batches and sending the recognized facial feature vectors of the plurality of persons to the stranger judgment module;
the stranger judging module is used for judging whether strangers exist in the multiple persons according to the facial feature vectors of the multiple persons, and if the strangers exist, a stranger existing signal is sent to the internal accompanying person judging module;
the internal accompanying person judging module is used for judging whether an internal accompanying person exists in a plurality of persons or not when a stranger signal is received, and if the internal accompanying person exists in the plurality of persons, sending the internal accompanying person signal to the security level analyzing module; if not, sending a signal without an internal accompanying person to the working period identification module;
the security level analysis module is used for acquiring the security level of the internal accompanying person from the internal accompanying person database according to the facial feature vector of the internal accompanying person when the internal accompanying person signal is received, determining the security level of a stranger according to the security level of the internal accompanying person and the number of corresponding persons, and sending the facial feature vector and the security level of the stranger to the VIP database module;
the visitant database module is used for storing the facial feature vector and the security level of a stranger;
the working period identification module is used for judging whether the working period is the working period or not when a signal of no internal accompanying person is received, and if the working period is the working period, sending an early warning signal to the early warning module; if not, sending an alarm signal to the early warning module;
the early warning module is used for early warning when receiving the early warning signal; and is used for alarming when receiving the alarm signal.
The process of determining the security level of a stranger by the security level analysis module comprises the following steps:
(1) judging whether 2 or more internal accompanying persons with the highest security level exist, and if so, determining the security level of a stranger as the highest security level; if not, entering the step (2);
(2) judging whether the handshake behavior of the stranger and the internal accompanying person exists, and if the handshake behavior of the stranger and the internal accompanying person exists, determining that the security level of the stranger is the same as that of the person with the highest security level in the internal accompanying person who shakes hands; if not, entering the step (3);
(3) judging whether a stranger stands still or not when an internal accompanying person speaks, and if the stranger stands still when the internal accompanying person speaks, determining that the security level of the stranger is equal to +1 of the highest security level of the internal accompanying person, and the highest security level cannot exceed the highest security level of the internal accompanying person; if the stranger moves the position, entering the step (4);
(4) according to the obtained position of the stranger in the internal accompanying person, if the stranger is positioned at two sides of the position, determining that the security level of the stranger is equal to that of the nearest internal accompanying person; if the position of the stranger is not on two sides or in the middle, determining that the security level of the stranger is equal to the maximum security level of the nearest internal accompanying person; and if the stranger is in the middle, determining that the security level of the stranger is the same as the maximum security level of all internal accompanying persons.
The invention is applied to the scene of realizing graded security management through the face access control equipment, and the low security level can not enter the high security level area, the building and the room. The organs or enterprises and institutions often have visitors to communicate, visit or visit.
The invention can automatically judge the security level of the visitor and allocate proper passing authority according to the identity and attitude of the internal accompanying person.
The invention utilizes the security level of the internal accompanying personnel and the body language of the internal accompanying personnel, and the human face recognition algorithm automatically recognizes the human body postures of the visitor and the internal accompanying personnel, automatically judges the security level of the guest, and then automatically adds the human face information of the guest into the human face access control system, so that the guest can smoothly communicate, visit and observe in a unit. The method not only ensures the free passage of the guest, but also can limit the guest from entering a high security level area, and prevents information leakage.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a flow chart of the present invention.
Detailed Description
The invention will be further described in detail with reference to the following drawings and specific examples, which are not intended to limit the invention, but are for clear understanding.
As shown in FIG. 1, the present invention relates to a guest pre-judging analysis system based on face recognition, which comprises
A guest pre-judging analysis system based on face recognition comprises
The face recognition module 1 is arranged at a unit entrance, and is used for recognizing and acquiring facial feature vectors of a plurality of persons entering the unit entrance in batch and sending the recognized facial feature vectors of the plurality of persons to the stranger judgment module;
the stranger judgment module 2 is used for judging whether strangers exist in the persons according to the facial feature vectors of the persons, and if the strangers exist, sending stranger existing signals to the internal accompanying person judgment module;
the internal accompanying person judging module 3 is used for judging whether an internal accompanying person exists in a plurality of persons or not when a stranger signal is received, and if the internal accompanying person exists, sending the internal accompanying person signal to the security level analyzing module; if not, sending a signal without an internal accompanying person to the working period identification module;
the security level analysis module 5 is used for acquiring the security level of the internal accompanying person from the internal accompanying person database according to the facial feature vector of the internal accompanying person when the internal accompanying person signal is received, determining the security level of a stranger according to the security level of the internal accompanying person and the number of corresponding persons, and sending the facial feature vector and the security level of the stranger to the VIP database module;
the visitant database module 7 is used for storing the facial feature vectors and the security level of strangers;
the working time interval identification module 4 is used for judging whether the working time interval is the working time interval or not when receiving a signal without an internal accompanying person, and if the working time interval is the working time interval, sending an early warning signal to the early warning module; if not, sending an alarm signal to the early warning module;
the early warning module 6 is used for early warning when receiving the early warning signal; and is used for alarming when receiving the alarm signal.
The process of determining the security level of a stranger by the security level analysis module comprises the following steps:
(1) judging whether 2 or more internal accompanying persons with the highest security level exist, and if so, determining the security level of a stranger as the highest security level; if not, entering the step (2);
(2) judging whether the handshake behavior of the stranger and the internal accompanying person exists, and if the handshake behavior of the stranger and the internal accompanying person exists, determining that the security level of the stranger is the same as that of the person with the highest security level in the internal accompanying person who shakes hands; if not, entering the step (3);
(3) judging whether a stranger stands still or not when an internal accompanying person speaks, and if the stranger stands still when the internal accompanying person speaks, determining that the security level of the stranger is equal to +1 of the highest security level of the internal accompanying person, and the highest security level cannot exceed the highest security level of the internal accompanying person; if the stranger moves the position, entering the step (4);
(4) according to the obtained position of the stranger in the internal accompanying person, if the stranger is positioned at two sides of the position, determining that the security level of the stranger is equal to that of the nearest internal accompanying person; if the position of the stranger is not on two sides or in the middle, determining that the security level of the stranger is equal to the maximum security level of the nearest internal accompanying person; and if the stranger is in the middle, determining that the security level of the stranger is the same as the maximum security level of all internal accompanying persons.
As shown in fig. 2, a guest prejudgment analysis method based on face recognition includes the following steps:
step 1: identifying and obtaining facial feature vectors of a plurality of persons entering an entrance of a unit in batch; the method comprises the steps that face recognition camera equipment is installed in an entrance hall or an entrance of a institution or an enterprise, the equipment has the function of recognizing faces and human body shapes in batches, when multiple pedestrians enter the entrance hall or the entrance, the pedestrians are captured by a camera of the equipment to recognize faces in batches, and feature vectors of different faces are obtained;
step 2: judging whether strangers exist in the persons according to the facial feature vectors of the persons, comparing the faces one by one, and judging whether the persons belong to internal accompanying persons (comparing the persons with data in an internal accompanying person database one by one);
and step 3: if a stranger exists, entering the step 4;
and 4, step 4: judging whether an internal accompanying person exists or not, if the internal accompanying person exists (whether the internal accompanying person exists in the same frame of the streaming media is read through a camera of the equipment), acquiring the security level of the internal accompanying person from an internal accompanying person database according to the facial feature vector of the internal accompanying person, determining the security level of a stranger according to the security level of the internal accompanying person and the number of corresponding persons, and entering the step 5; if no internal accompanying person exists, entering step 6;
and 5: adding the facial feature vector and the security level of the stranger to a database of the visitant group;
step 6: judging whether the working time is a working time period, and if so, early warning in real time; if not, real-time alarming is carried out.
In the step 4: judging whether 2 or more internal accompanying persons with the highest security level exist, and if so, determining the security level of the stranger as the highest security level; if not, judging whether a handshake behavior of a stranger and an internal accompanying person exists or not.
In the step 4: if the handshake behavior of the stranger and the internal accompanying person exists, determining that the security level of the stranger is the same as that of the person with the highest security level in the internal accompanying person who shakes hands; if not, whether a stranger stands still or not is judged when the internal accompanying person speaks.
And judging whether the handshake action exists between the stranger and the internal accompanying person through a human face and body recognition algorithm, namely whether the hands of the stranger and the hands of the internal accompanying person overlap in the same picture, and if the hands of the stranger and the hands of the internal accompanying person overlap, judging that the security level of the stranger is the same as the highest security level of the internal accompanying person who shakes the handshake.
In the step 4: when the internal accompanying person speaks (judged by the change of the shape of the lips), if the stranger stands still, the security level of the stranger is determined to be equal to +1, the highest security level of the internal accompanying person, and the highest security level cannot exceed the highest security level of the internal accompanying person; and if the stranger moves the position, acquiring the position of the stranger in the internal accompanying person.
In the step 4: if the strangers are located on two sides of the position, determining that the security level of the strangers is equal to that of the nearest internal accompanying person; if the position of the stranger is not on two sides or in the middle, determining that the security level of the stranger is equal to the maximum security level of the nearest internal accompanying person; and if the stranger is in the middle, determining that the security level of the stranger is the same as the maximum security level of all internal accompanying persons.
Those not described in detail in this specification are within the skill of the art.
Claims (9)
1. A guest prejudgment analysis method based on face recognition is characterized in that: the method comprises the following steps:
step 1: identifying and obtaining facial feature vectors of a plurality of persons entering an entrance of a unit in batch;
step 2: judging whether strangers exist in the multiple persons or not according to the facial feature vectors of the multiple persons;
and step 3: if a stranger exists, entering the step 4;
and 4, step 4: judging whether an internal accompanying person exists or not, if so, acquiring the security level of the internal accompanying person from an internal accompanying person database according to the facial feature vector of the internal accompanying person, determining the security level of a stranger according to the security level of the internal accompanying person and the number of corresponding persons, and entering step 5; if no internal accompanying person exists, entering step 6;
and 5: adding the facial feature vector and the security level of the stranger to a database of the visitant group;
step 6: judging whether the working time is a working time period, and if so, early warning in real time; if not, real-time alarming is carried out.
2. The guest anticipation analysis method based on face recognition according to claim 1, wherein: in the step 4: judging whether 2 or more internal accompanying persons with the highest security level exist, and if so, determining the security level of the stranger as the highest security level; if not, judging whether a handshake behavior of a stranger and an internal accompanying person exists or not.
3. The guest anticipation analysis method based on face recognition according to claim 2, wherein: in the step 4: if the handshake behavior of the stranger and the internal accompanying person exists, determining that the security level of the stranger is the same as that of the person with the highest security level in the internal accompanying person who shakes hands; if not, whether a stranger stands still or not is judged when the internal accompanying person speaks.
4. The guest anticipation analysis method based on face recognition according to claim 3, wherein: in the step 4: when an internal accompanying person speaks, if a stranger does not stand, determining that the security level of the stranger is equal to +1 of the highest security level of the internal accompanying person, and the highest security level cannot exceed the highest security level of the internal accompanying person; and if the stranger moves the position, acquiring the position of the stranger in the internal accompanying person.
5. The guest anticipation analysis method based on face recognition according to claim 4, wherein: in the step 4: if the strangers are located on two sides of the position, determining that the security level of the strangers is equal to that of the nearest internal accompanying person; if the position of the stranger is not on two sides or in the middle, determining that the security level of the stranger is equal to the maximum security level of the nearest internal accompanying person; and if the stranger is in the middle, determining that the security level of the stranger is the same as the maximum security level of all internal accompanying persons.
6. The guest anticipation analysis method based on face recognition according to claim 1, wherein: in the step 1, facial feature vectors of a plurality of persons entering an entrance of a unit within a preset time t are identified and acquired in batch.
7. The guest anticipation analysis method based on face recognition according to claim 6, wherein: the preset time t ranges from 0.3 to 3 seconds.
8. The utility model provides a guest is judged analytic system in advance based on face identification which characterized in that: comprises that
The face recognition module is arranged at the entrance of the unit and used for recognizing and acquiring facial feature vectors of a plurality of persons entering the entrance of the unit in batches and sending the recognized facial feature vectors of the plurality of persons to the stranger judgment module;
the stranger judging module is used for judging whether strangers exist in the multiple persons according to the facial feature vectors of the multiple persons, and if the strangers exist, a stranger existing signal is sent to the internal accompanying person judging module;
the internal accompanying person judging module is used for judging whether an internal accompanying person exists in a plurality of persons or not when a stranger signal is received, and if the internal accompanying person exists in the plurality of persons, sending the internal accompanying person signal to the security level analyzing module; if not, sending a signal without an internal accompanying person to the working period identification module;
the security level analysis module is used for acquiring the security level of the internal accompanying person from the internal accompanying person database according to the facial feature vector of the internal accompanying person when the internal accompanying person signal is received, determining the security level of a stranger according to the security level of the internal accompanying person and the number of corresponding persons, and sending the facial feature vector and the security level of the stranger to the VIP database module;
the visitant database module is used for storing the facial feature vector and the security level of a stranger;
the working period identification module is used for judging whether the working period is the working period or not when a signal of no internal accompanying person is received, and if the working period is the working period, sending an early warning signal to the early warning module; if not, sending an alarm signal to the early warning module;
the early warning module is used for early warning when receiving the early warning signal; and is used for alarming when receiving the alarm signal.
9. The system of claim 8, wherein the system comprises: the process of determining the security level of a stranger by the security level analysis module comprises the following steps:
(1) judging whether 2 or more internal accompanying persons with the highest security level exist, and if so, determining the security level of a stranger as the highest security level; if not, entering the step (2);
(2) judging whether the handshake behavior of the stranger and the internal accompanying person exists, and if the handshake behavior of the stranger and the internal accompanying person exists, determining that the security level of the stranger is the same as that of the person with the highest security level in the internal accompanying person who shakes hands; if not, entering the step (3);
(3) judging whether a stranger stands still or not when an internal accompanying person speaks, and if the stranger stands still when the internal accompanying person speaks, determining that the security level of the stranger is equal to +1 of the highest security level of the internal accompanying person, and the highest security level cannot exceed the highest security level of the internal accompanying person; if the stranger moves the position, entering the step (4);
(4) according to the obtained position of the stranger in the internal accompanying person, if the stranger is positioned at two sides of the position, determining that the security level of the stranger is equal to that of the nearest internal accompanying person; if the position of the stranger is not on two sides or in the middle, determining that the security level of the stranger is equal to the maximum security level of the nearest internal accompanying person; and if the stranger is in the middle, determining that the security level of the stranger is the same as the maximum security level of all internal accompanying persons.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110874878A (en) * | 2018-08-09 | 2020-03-10 | 深圳云天励飞技术有限公司 | Pedestrian analysis method, device, terminal and storage medium |
| CN111414799A (en) * | 2020-02-14 | 2020-07-14 | 北京三快在线科技有限公司 | Method and device for determining peer users, electronic equipment and computer readable medium |
| CN112100423A (en) * | 2020-08-10 | 2020-12-18 | 重庆锐云科技有限公司 | Real estate case client visit management system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10289822B2 (en) * | 2016-07-22 | 2019-05-14 | Nec Corporation | Liveness detection for antispoof face recognition |
| US20210080926A1 (en) * | 2019-09-12 | 2021-03-18 | Ebay Inc. | Systems and methods for deploying a robotic security escort to enhance enterprise security |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110874878A (en) * | 2018-08-09 | 2020-03-10 | 深圳云天励飞技术有限公司 | Pedestrian analysis method, device, terminal and storage medium |
| CN111414799A (en) * | 2020-02-14 | 2020-07-14 | 北京三快在线科技有限公司 | Method and device for determining peer users, electronic equipment and computer readable medium |
| CN112100423A (en) * | 2020-08-10 | 2020-12-18 | 重庆锐云科技有限公司 | Real estate case client visit management system and method |
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