Interference Detection System and Device

Abstract

The present application discloses an interference detection system and device. The interference detection system includes a plurality of information collection devices for collecting device information and face images; and an electronic device for obtaining M sets of collision counts based on the device information and face images collected by the plurality of information collection devices. The M sets of collision counts correspond one-to-one to M device identifiers, and each set of collision counts includes the number of collisions between the corresponding device identifier and N user identifiers, where both M and N are positive integers. The electronic device is further configured to determine M probability sets based on the M sets of collision counts, and the M device identifiers correspond one-to-one to the M probability sets. The electronic device is further configured to determine an interference identifier based on the M probability sets, and the interference identifier is included in the N user identifiers. The embodiments of the present application can improve the efficiency of determining the interference identifier.

Description

Technical Field

[0001] This application relates to the field of electronic technologies, and in particular, to an interference detection system and device. Background Art

[0002] With the rapid development and popularization of the Internet, the types and quantities of application software have been continuously increasing, especially in the field of terminal devices, which has brought a lot of convenience to people's lives. Therefore, the connection between people and terminal devices has been continuously strengthened. Currently, through technologies such as the detection, recognition, positioning, and tracking of targets in the fields of machine vision and image processing, the matching relationship between a terminal device and a user can be determined, that is, whether the terminal device belongs to the user. However, when irrelevant terminal devices continuously appear during the process of determining the matching relationship, it will lead to incorrect matching results, and it is necessary to manually determine the irrelevant terminal devices that cause interference based on the matching relationship. Summary of the Invention

[0003] An embodiment of this application provides an interference detection system to automatically determine interference identifiers by a machine, thereby improving the efficiency of determining interference identifiers.

[0004] In a first aspect, an embodiment of this application provides an interference detection system, and the interference detection system includes:

[0005] A plurality of information collection devices, configured to collect device information and face images; and

[0006] An electronic device, configured to obtain M sets of collision times based on the device information and face images collected by the plurality of information collection devices, the M sets of collision times corresponding to M device identifiers one by one, the j-th set of collision times including the number of times the j-th device identifier collides with N user identifiers respectively, where M and N are both positive integers, and 1 ≤ j ≤ M;

[0007] The electronic device is further configured to determine M sets of probabilities based on the M sets of collision times, the M device identifiers corresponding to the M sets of probabilities one by one;

[0008] The electronic device is further configured to determine an interference identifier based on the M sets of probabilities, and the interference identifier is included in the N user identifiers.

[0009] In a second aspect, an embodiment of this application provides an interference detection device, which is applied to an electronic device in an interference detection system. The device includes:

[0010] An acquisition unit, configured to obtain M sets of collision times based on device information and face images collected by multiple information collection devices, where the M sets of collision times correspond one-to-one to M device identifiers, and the j-th set of collision times includes the number of collisions between the j-th device identifier and N user identifiers respectively, where M and N are both positive integers, and 1 ≤ j ≤ M;

[0011] A first determination unit, configured to determine M probability sets based on the M sets of collision times, where the M device identifiers correspond one-to-one to the M probability sets;

[0012] A second determination unit, configured to determine an interference identifier based on the M probability sets, where the interference identifier is included in the N user identifiers.

[0013] It can be seen that in the embodiments of the present application, the electronic device in the interference detection system first obtains M sets of collision times based on the device information and face images collected by multiple information collection devices, then determines M probability sets based on the M sets of collision times, and finally determines the interference identifier based on the M probability sets. Since the electronic device in the interference detection system automatically determines the interference identifier based on the M probability sets and does not require manual determination of the interference identifier, the efficiency of determining the interference identifier is improved. Description of the Drawings

[0014] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0015] Figure 1A is a schematic structural diagram of an interference detection system provided by an embodiment of the present application;

[0016] Figure 1B is a schematic structural diagram of an information collection device provided by an embodiment of the present application;

[0017] Figure 2 A schematic structural diagram of an interference detection device provided by an embodiment of the present application. Detailed Embodiments

[0018] To enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0019] The following will be described in detail respectively.

[0020] The terms "first", "second", "third", "fourth", etc. in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0021] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0022] The following will explain some terms in this application to facilitate the understanding of those skilled in the art.

[0023] Please refer to Figure 1A , Figure 1A which is an interference detection system provided by an embodiment of this application. The interference detection system includes an electronic device and a plurality of information collection devices provided at a plurality of information collection points. Figure 1A The forms and quantities of the electronic device and the information collection devices shown in

[0024] are only for illustration and do not constitute a limitation to the embodiments of this application.

[0025] Among them, the positions of the information collection points can be preset, and the positions of different information collection points are different.

[0026] An electronic device may include various handheld devices, vehicle-mounted devices, wearable devices, computing devices, or other processing devices connected to a wireless modem that have wireless communication capabilities, as well as various forms of user equipment (UE), mobile stations (MS), and so on.

[0027] Please refer to Figure 1B , Figure 1B FIG. Figure 1B is a schematic structural diagram of an information collection device provided by an embodiment of the present application. The information collection device includes a face image collection module 101 and a device information collection module 102.

[0028] Among them, the face image collection module 101 is used to collect face images within a preset area.

[0029] Among them, the device information collection module 102 is used to collect device information within a preset area.

[0030] Among them, the device information collection module may include a WIFI module, a LIFI module, and so on.

[0031] Among them, the face image collection module may include a camera module, a monitoring module, and so on.

[0032] The embodiments of the present application will be introduced in detail below.

[0033] The embodiments of the present application provide an interference detection system, and the interference detection system includes:

[0034] Multiple information collection devices, which are used to collect device information and face images.

[0035] An electronic device, which is used to obtain M collision count sets based on the device information and face images collected by the multiple information collection devices. The M collision count sets correspond one-to-one with M device identifiers. The jth collision count set includes the number of collisions between the jth device identifier and N user identifiers respectively. Both M and N are positive integers, and 1 ≤ j ≤ M.

[0036] Among them, the device identifier is unique and can be a letter, a number, and so on.

[0037] Among them, the user identifier is unique and can be the number information of the user.

[0038] Among them, M device identifiers can also be replaced with M user identifiers, and N user identifiers can be replaced with N device identifiers. The method for detecting interference identifiers among N user identifiers is the same as that of the present application and will not be described herein again.

[0039] Among them, the number of collisions between the device identifier and N user identifiers respectively may be the same or different.

[0040] Among them, the number of times the device identifier collides with each of the N user identifiers can be 0 times or not 0 times.

[0041] For example, the device identifier is A, there are 4 user identifiers (B1, B2, B3, and B4), the number of times A collides with B1 is 5 times, the number of times A collides with B2 is 3 times, the number of times A collides with B3 is 1 time, and the number of times A collides with B4 is 0 times.

[0042] Among them, each device identifier corresponds to the same N user identifiers.

[0043] The electronic device is further configured to determine M probability sets based on the M collision count sets, and the M device identifiers correspond to the M probability sets one by one.

[0044] Among them, each probability set includes N probability values, and the N probability values correspond to the N user identifiers one by one.

[0045] Among them, the N user identifiers corresponding to the N probability values in each probability set are the same.

[0046] For example, there are 2 probability sets (#1 and #2), 3 user identifiers (B1, B2, and B3), probability set #1 is {0.3, 0.45, 0.09}, probability set #2 is {0.23, 0.35, 0}, then the probability value 0.3 and the probability value 0.23 both correspond to the user identifier B1, the probability values 0.45 and 0.35 both correspond to the user identifier B2, and the probability values 0.09 and 0 both correspond to the user identifier B3.

[0047] Among them, the probability values included in each probability set are used to indicate the matching relationship between the user identifier corresponding to the probability value and the device identifier corresponding to the probability value. The matching relationship can be abnormal or normal. When the matching relationship is abnormal, the user identifier corresponding to the matching relationship is an interference identifier, and an abnormal matching relationship means that the terminal devices corresponding to the device identifier do not belong to the users corresponding to the interference identifier.

[0048] For example, there is one device identifier A1 and three user identifiers (B1, B2, and B3). The probability set corresponding to the device identifier A1 is #1. Therefore, the probability set includes three probability values. Assume that the relationship between the device identifier corresponding to the probability value less than 0.1 in #1 and the user identifier is abnormal, and the corresponding user identifier is an interference identifier. If the probability set #1 is {0.7, 0.2, 0.09}, then the probability value 0.7 in the probability set #1 corresponds to B1, the probability value 0.2 corresponds to B2, and the probability value 0.09 corresponds to B3. Since 0.09 is less than 0.1, it can be determined that B3 is an interference identifier, and the relationship between A1 and B3 is abnormal.

[0049] The electronic device is further configured to determine an interference identifier based on the M probability sets, and the N user identifiers include the interference identifier.

[0050] Among them, one interference identifier can be determined by each probability set, or one interference identifier can be determined by multiple probability sets.

[0051] It can be seen that in the embodiment of the present application, the electronic device in the interference detection system first obtains M collision count sets, then determines M probability sets based on the M collision count sets, and finally determines the interference identifier based on the M probability sets. Since the electronic device in the interference detection system determines the interference identifier based on the M probability sets and does not require manual determination of the interference identifier, the efficiency of determining the interference identifier is improved.

[0052] In an implementation manner of the present application, the multiple information collection devices are arranged at multiple information collection points. Each information collection device is configured to collect device information and face images in a preset area, and record the time when the device information is collected and the time when the face images are collected.

[0053] The target information collection device is configured to collect T device information and S face images, and record the time when the T device information is collected and the time when the S face images are collected. The multiple information collection devices include the target information collection device, and both T and S are positive integers.

[0054] The electronic device is further configured to obtain the T device information, the S face images, and the time when the T device information is collected and the time when the S face images are collected before obtaining the M collision count sets.

[0055] The electronic device is further configured to determine the M device identifiers based on the T device information. Each device identifier corresponds to at least one of the device information, and T is greater than or equal to M.

[0056] The electronic device is further configured to perform face recognition on the S face images to determine the N user identities, where each user identity corresponds to at least one of the face images, and S is greater than or equal to N.

[0057] The electronic device is further configured to determine, based on the first collision principle, the time when the T device information is collected, and the time when the S face images are collected, the number of collisions between each of the M device identities and the N user identities, to obtain M sets of collision times.

[0058] Wherein, the first collision principle is: if the time difference between the first time and the second time is less than the first duration, and the device information acquisition module that acquires the device information corresponding to the device identity and the face image acquisition module that acquires the face image corresponding to the user identity belong to the same information acquisition device, then a collision occurs between the device identity and the user identity. The first time is the time when the device information acquisition module acquires the device information, and the second time is the time when the face image acquisition module acquires the face image.

[0059] Wherein, the information acquisition device can record the acquisition time of the device information and the acquisition time of the face image in a table.

[0060] Wherein, the target information acquisition device can be one or multiple.

[0061] Wherein, when there are multiple target information acquisition devices, each target information acquisition device records the acquisition time of the acquired device information and the acquisition time of the acquired face image in the corresponding table.

[0062] Wherein, when there are multiple target information acquisition devices, the acquisition times of the T device information may be recorded in different tables, and the acquisition times of the S face images may be recorded in different tables.

[0063] Wherein, the device information is unique and can be the Media Access Control (MAC) address of the device or other identifiers.

[0064] Wherein, there may be the same device information among the T device information, or there may be no same device information.

[0065] Wherein, there may be the same face image among the S face images, or there may be no same face image.

[0066] Wherein, the device identity is unique.

[0067] Among them, if one device identifier corresponds to multiple device information, the corresponding multiple device information is the same; if a user identifier corresponds to multiple face images, the corresponding multiple face images belong to the same user.

[0068] Among them, the first duration can be preset. The first duration can be 5s, 8s, 9s, and so on.

[0069] For example, assume that there are 2 information collection devices (A and B), device identifier 1 corresponds to 2 device information (#11 and #12), user identifier 2 corresponds to 3 face images (#21, #22, and #23). If the device information collection module of information collection device A collects device information #11 at collection time a, the device information collection module of information collection device B collects device information #12 at collection time b, the face image collection module of information collection device A collects face image #21 at collection time a, the face image collection module of information collection device A collects face image #22 at collection time b, and the face image collection module of information collection device B collects face image #23 at collection time b, then it is determined that the number of collisions between device identifier 1 and user identifier 2 is 2 times.

[0070] It can be seen that in the embodiment of the present application, the electrons in the interference detection system determine M collision number sets through T device information and S face images, which is beneficial to improving the performance of the interference detection system.

[0071] In an implementation manner of the present application, in terms of determining M probability sets based on the M collision number sets, the electronic device is specifically configured to:

[0072] Determine a collision probability set based on each of the collision number sets to obtain M collision probability sets;

[0073] Determine N inverse collision probabilities based on the M collision number sets, and the N inverse collision probabilities correspond to the N user identifiers one by one;

[0074] Determine M probability sets based on the M collision probability sets and the N inverse collision probabilities.

[0075] Among them, the sum of the collision probabilities in each collision probability set is 1.

[0076] Among them, the collision probability sets determined by each collision number set are different.

[0077] Among them, the number of collision probabilities included in each collision probability set is N, and the N collision probabilities correspond to the N user identifiers one by one.

[0078] Among them, the collision probabilities included in each collision probability set are used to represent the probabilities of collisions between the corresponding user identifiers and the device identifiers corresponding to each collision probability set respectively.

[0079] For example, there are 3 device identifiers (A1, A2, and A3), 3 user identifiers (B1, B2, and B3), and 3 device identifiers corresponding to 3 collision probability sets (#1, #2, and #3). Therefore, each collision probability set includes 3 collision probabilities. Suppose the collision probability set #1 corresponding to A1 is {0.7, 0.2, 0.1}, the collision probability set #2 corresponding to A2 is {0.2, 0.7, 0.1}, and the collision probability set #3 corresponding to A3 is {0.1, 0.3, 0.6}. Then the probability of B1 colliding with A1 is 0.7, the probability of B2 colliding with A1 is 0.2, the probability of B3 colliding with A1 is 0.1. The probability of B1 colliding with A2 is 0.2, the probability of B2 colliding with A2 is 0.7, the probability of B3 colliding with A1 is 0.1. The probability of B1 colliding with A3 is 0.1, the probability of B2 colliding with A3 is 0.3, and the probability of B3 colliding with A3 is 0.6.

[0080] Among them, the N inverse collision probabilities can be the same or different.

[0081] Among them, the inverse collision probability is used to represent the probability of collision between the corresponding user identifier and the M device identifiers.

[0082] Among them, the number of values included in each probability set is the same.

[0083] Among them, the M probability sets are different from each other.

[0084] It can be seen that in the embodiments of the present application, the electronic device determines M probability sets through M collision probability sets and N inverse collision probabilities. Since the electronic device determines the probability sets through statistical methods, it is beneficial to the accuracy of the electronic device in determining the probability sets.

[0085] In an implementation manner of the present application, in terms of determining a collision probability set based on each of the collision count sets to obtain M collision probability sets, the electronic device is specifically configured to:

[0086] Determine a total collision count based on each collision count set to obtain M total collision counts, where the j-th total collision count is used to represent the total number of collisions between the j-th device identifier and the N user identifiers;

[0087] Determine a collision probability set based on the i-th collision count included in each collision count set and the total collision count corresponding to each collision count set to obtain M collision probability sets, where 1 ≤ i ≤ N.

[0088] Among them, the total number of collisions determined by each set of collision times can be the same or different.

[0089] Optionally, determining a set of collision probabilities based on the i-th collision number included in each set of collision times and the total number of collisions corresponding to each set of collision times includes:

[0090] Determining a set of collision probabilities based on the i-th collision number included in each set of collision times, the total number of collisions corresponding to each set of collision times, and a first formula.

[0091] Among them, the first formula is P1 = S / T, where P1 is the collision probability, S is the number of collisions, and T is the total number of collisions.

[0092] For example, there are 3 sets of collision times (#1, #2, and #3). The set of collision times #1 is {2, 9, 7}, the set of collision times #2 is {8, 9, 2}, and the set of collision times #3 is {5, 9, 1}. Then the total number of collisions for the set of collision times #1 is 18, the total number of collisions for the set of collision times #2 is 19, and the total number of collisions for the set of collision times #3 is 15. For the set of collision times #1, the determined set of collision probabilities is {2 / 18, 9 / 18, 7 / 18}; for the set of collision times #2, the determined set of collision probabilities is {8 / 19, 9 / 19, 2 / 19}, and for the set of collision times #3, the determined set of collision probabilities is {5 / 15, 9 / 15, 1 / 15}.

[0093] It can be seen that in the embodiments of the present application, determining a set of collision probabilities based on the i-th collision number included in each set of collision times and the total number of collisions corresponding to each set of collision times is beneficial for the interference detection system to narrow the range where the interference identifier is located based on the collision probability.

[0094] In an implementation manner of the present application, in terms of determining N inverse collision probabilities based on the M sets of collision times, the electronic device is specifically configured to:

[0095] Determining N quantities based on the M sets of collision times, the N quantities corresponding one-to-one to the N user identifiers, and the i-th quantity being used to represent the number of device identifiers that collide with the i-th user identifier in the M sets of collision times;

[0096] Determining N inverse collision probabilities based on the N quantities and the M, where the i-th inverse collision probability is used to represent the probability that the M device identifiers collide with the i-th user identifier.

[0097] Among them, the N quantities can be the same or different.

[0098] Optionally, determining the N inverse collision probabilities based on the N quantities and the M includes:

[0099] Determining each inverse collision probability based on each quantity, the M, and a second formula, to obtain M inverse collision probabilities.

[0100] Wherein, the second formula is P2 = log(Num / (M + 1)), P2 is the inverse collision probability, Num is the number of device identifiers that collide with the user identifier, and M is the number of device identifiers.

[0101] For example, there are 3 collision count sets (#1, #2, and #3), 3 user identifiers (B1, B2, and B3), the collision count set #1 is {0, 9, 7}, the collision count set #2 is {8, 9, 0}, the collision count set #3 is {5, 9, 1}, the collision count 0 in the collision count set #1, the collision count 8 in the collision count set #2, and the collision count 5 in the collision count set #3 all correspond to B1, the collision count 9 in the collision count set #1, the collision count 9 in the collision count set #2, and the collision count 9 in the collision count set #3 all correspond to B2, and the collision counts 7 in the collision count set #1, 0 in the collision count set #2, and 1 in the collision count set #3 correspond to B3 respectively. Then the quantities corresponding to B1 and B2 are both 2, and the quantity corresponding to B3 is 3. Therefore, the inverse collision probabilities corresponding to B1 and B2 are both log(2 / 4), and the inverse collision probability corresponding to B3 is log(3 / 4).

[0102] It can be seen that in the embodiments of the present application, N quantities are determined based on the M collision count sets, and N inverse collision probabilities are determined based on the N quantities. Since the larger the inverse collision probability, the fewer the device identifiers that collide with the user identifier, it is beneficial for the interference detection system to narrow the range where the interference identifier is located based on the inverse collision probability.

[0103] In an implementation manner of the present application, in terms of determining the M probability sets based on the M collision probability sets and the N inverse collision probabilities, the electronic device is specifically configured to:

[0104] Determine one probability set based on the i-th collision probability included in each collision probability set and the i-th inverse collision probability among the N inverse collision probabilities, to obtain M probability sets. The i-th probability value in the j-th probability set is the product of the i-th collision probability in the j-th collision probability set and the i-th inverse collision probability among the N inverse collision probabilities. The i-th probability value in the j-th probability set is used to indicate the matching relationship between the j-th device identifier and the i-th user identifier.

[0105] Optionally, based on the i-th collision probability included in each set of collision probabilities and the i-th inverse collision probability among the N inverse collision probabilities, determine a set of probabilities, including:

[0106] Based on the i-th collision probability included in each set of collision probabilities, the i-th inverse collision probability among the N inverse collision probabilities, and a third formula, determine a set of probabilities.

[0107] Where E = P1 * P2, E is the probability value of the user identifier, P1 is the collision probability of the user identifier, P2 is the inverse collision probability of the user identifier, and the objects corresponding to P1 and P2 are the same user identifier.

[0108] For example, there are 3 device identifiers (A1, A2, and A3), the 3 device identifiers correspond one-to-one with 3 sets of collision probabilities (#1, #2, and #3), and 3 user identifiers (B1, B2, and B3). Therefore, each set of collision probabilities includes 3 collision probabilities. Assume that the set of collision probabilities #1 corresponding to A1 is {0.7, 0.2, 0.1}, the set of collision probabilities #2 corresponding to A2 is {0.2, 0.7, 0.1}, and the set of collision probabilities #3 corresponding to A3 is {0.1, 0.3, 0.6}. Then the collision probability 0.7 in the set of collision probabilities #1, the collision probability 0.2 in #2, and the collision probability 0.1 in #3 all correspond to B1; the collision probability 0.2 in the set of collision probabilities #1, the collision probability 0.7 in #2, and the collision probability 0.3 in #3 all correspond to B2; the collision probability 0.1 in the set of collision probabilities #1, the collision probability 0.1 in #2, and the collision probability 0.6 in #3 all correspond to B3. Assume that the inverse collision probability corresponding to B1 is 0.199, the inverse collision probability corresponding to B2 is 0.21, and the inverse collision probability corresponding to B3 is 0.37. Then the set of probabilities corresponding to the set of collision probabilities #1 is {0.7 * 0.199, 0.2 * 0.21, 0.1 * 0.37}, the set of probabilities corresponding to the set of collision probabilities #2 is {0.2 * 0.199, 0.7 * 0.21, 0.1 * 0.37}, and the set of probabilities corresponding to the set of collision probabilities #3 is {0.1 * 0.199, 0.3 * 0.21, 0.6 * 0.37}.

[0109] It can be seen that in the embodiments of the present application, since the smaller the probability value in the set of probabilities indicates the greater the probability of the interference identifier, the interference identifier can be quickly determined based on the set of probabilities.

[0110] In one implementation manner of the present application, in terms of determining the interference identifier based on the M sets of probabilities, the electronic device is specifically configured to:

[0111] Determine the target probability values lower than a preset threshold in each set of probabilities;

[0112] Determine that the user identifier corresponding to the target probability value is an interference identifier.

[0113] Wherein, the preset thresholds corresponding to each probability set are the same.

[0114] Wherein, there can be one or more interference identifiers.

[0115] It can be seen that in the embodiment of the present application, determining the interference identifier through the probability set and the preset threshold is beneficial to improving the efficiency of determining the interference identifier.

[0116] Please refer to Figure 2 , Figure 2 which is an interference detection device provided by an embodiment of the present application, and is applied to an electronic device in an interference detection system. The device includes:

[0117] An acquisition unit 201, configured to acquire M collision count sets based on the device information and face images acquired by the multiple information acquisition devices. The M collision count sets correspond one-to-one to M device identifiers. The jth collision count set includes the number of collisions between the jth device identifier and N user identifiers respectively. Both M and N are positive integers, and 1 ≤ j ≤ M;

[0118] A first determination unit 202, configured to determine M probability sets based on the M collision count sets. The M device identifiers correspond one-to-one to the M probability sets;

[0119] A second determination unit 203, configured to determine an interference identifier based on the M probability sets. The N user identifiers include the interference identifier.

[0120] In an implementation manner of the present application, the interference detection device further includes a receiving unit 204.

[0121] In an implementation manner of the present application, before acquiring the M collision count sets, the receiving unit 204 is specifically configured to execute the following step instructions:

[0122] Acquire the T device information, the S face images, the time when the T device information is acquired, and the time when the S face images are acquired. The T device information and the S face images are acquired by a target signal acquisition device among the multiple information acquisition devices at multiple information acquisition points within a preset area. The time when the T device information is acquired and the time when the S face images are acquired are recorded by the target signal acquisition device. Both T and S are positive integers;

[0123] Based on the T device information, determine the M device identifiers. Each device identifier corresponds to at least one piece of the device information, and T is greater than or equal to M;

[0124] Perform face recognition on the S face images to determine the N user identifiers, where each user identifier corresponds to at least one of the face images, and S is greater than or equal to N.

[0125] Based on the first collision principle, the moment when the T device information is collected, and the moment when the S face images are collected, determine the number of collisions between each of the M device identifiers and the N user identifiers, obtaining M collision count sets; where the first collision principle is: if the time difference length between the first moment and the second moment is less than the first duration, and the device information acquisition module that collects the device information corresponding to the device identifier and the face image acquisition module that collects the face image corresponding to the user identifier belong to the same information acquisition device, then a collision occurs between the device identifier and the user identifier. The first moment is the moment when the device information acquisition module collects the device information corresponding to the device identifier, and the second moment is the moment when the face image acquisition module collects the face image corresponding to the user identifier.

[0126] In an implementation manner of the present application, in terms of determining M probability sets based on the M collision count sets, the above-mentioned first determination unit 202 is specifically used to execute the following step instructions:

[0127] Determine a collision probability set based on each collision count set, obtaining M collision probability sets;

[0128] Determine N inverse collision probabilities based on the M collision count sets, where the N inverse collision probabilities correspond one-to-one to the N user identifiers;

[0129] Determine M probability sets based on the M collision probability sets and the N inverse collision probabilities.

[0130] In an implementation manner of the present application, in terms of determining a collision probability set based on each collision count set and obtaining M collision probability sets, the above-mentioned first determination unit 202 is specifically used to execute the following step instructions:

[0131] Determine a total collision count based on each collision count set, obtaining M total collision counts. The jth total collision count is used to represent the total number of collisions between the jth device identifier and the N user identifiers;

[0132] Determine a collision probability set based on the ith collision count included in each collision count set and the total collision count corresponding to each collision count set, obtaining M collision probability sets, where 1 ≤ i ≤ N.

[0133] In an implementation manner of the present application, when determining N inverse collision probabilities based on the M collision count sets, the above-mentioned first determination unit 202 is specifically configured to execute the following step instructions:

[0134] Determine N quantities based on the M collision count sets, where the N quantities are in one-to-one correspondence with the N user identifiers, and the i-th quantity is used to represent the number of device identifiers that collide with the i-th user identifier;

[0135] Determine N inverse collision probabilities based on the N quantities and the M. The i-th inverse collision probability is used to represent the probability that the M device identifiers collide with the i-th user identifier.

[0136] In an implementation manner of the present application, when determining M probability sets based on the M collision probability sets and the N inverse collision probabilities, the above-mentioned first determination unit 202 is specifically configured to execute the following step instructions:

[0137] Determine a probability set based on the i-th collision probability included in each collision probability set and the i-th inverse collision probability among the N inverse collision probabilities, and obtain M probability sets. The i-th probability value in the j-th probability set is the product of the i-th collision probability in the j-th collision probability set and the i-th inverse collision probability among the N inverse collision probabilities. The i-th probability value in the j-th probability set is used to indicate the matching relationship between the j-th device identifier and the i-th user identifier.

[0138] In an implementation manner of the present application, when determining interference identifiers based on the M probability sets, the above-mentioned second determination unit 203 is specifically configured to execute the following step instructions:

[0139] Determine the target probability values that are lower than the preset threshold in each probability set;

[0140] Determine that the user identifier corresponding to the target probability value is an interference identifier.

[0141] It should be noted that the acquisition unit 201, the first determination unit 202, the second determination unit 203, and the receiving unit 204 can be implemented by a processor.

Claims

1. An interference detection system, characterized in that, the interference detection system includes: a plurality of information collection devices for collecting device information and face images; and an electronic device for obtaining M sets of collision times based on the device information and face images collected by the plurality of information collection devices, the M sets of collision times corresponding one-to-one to M device identifiers, the j-th set of collision times including the number of collisions between the j-th device identifier and N user identifiers respectively, where M and N are both positive integers, and 1 ≤ j ≤ M; the electronic device is further configured to determine M probability sets based on the M sets of collision times, the M device identifiers corresponding one-to-one to the M probability sets; the electronic device is further configured to determine an interference identifier based on the M probability sets, the interference identifier being included in the N user identifiers; In terms of determining the M probability sets based on the M sets of collision times, the electronic device specifically is configured to: determine a set of collision probabilities for each set of collision times to obtain M sets of collision probabilities; determine N inverse collision probabilities based on the M sets of collision times, the N inverse collision probabilities corresponding one-to-one to the N user identifiers; determine M probability sets based on the M sets of collision probabilities and the N inverse collision probabilities.

2. The system according to claim 1, characterized in that, the plurality of information collection devices are arranged at a plurality of information collection points, each information collection device being configured to collect device information and face images within a preset area, and record the time when the device information is collected and the time when the face image is collected; a target information collection device for collecting T device information and S face images, and recording the time when the T device information is collected and the time when the S face images are collected, the plurality of information collection devices including the target information collection device, where T and S are both positive integers; the electronic device is further configured to obtain the T device information, the S face images, the time when the T device information is collected, and the time when the S face images are collected before obtaining the M sets of collision times; the electronic device is further configured to determine the M device identifiers based on the T device information, each device identifier corresponding to at least one of the device information, where T is greater than or equal to M; the electronic device is further configured to perform face recognition on the S face images to determine the N user identifiers, each user identifier corresponding to at least one of the face images, where S is greater than or equal to N; the electronic device is further configured to determine the number of collisions between each of the M device identifiers and the N user identifiers respectively based on a first collision principle, the time when the T device information is collected, and the time when the S face images are collected, to obtain M sets of collision times.

3. The system according to claim 2, characterized in that, The first collision principle is as follows: If the time difference length between the first moment and the second moment is less than the first time length, and the device information acquisition module that acquires the device information corresponding to the device identifier and the face image acquisition module that acquires the face image corresponding to the user identifier belong to the same information acquisition device, then a collision occurs between the device identifier and the user identifier. The first moment is the moment when the device information acquisition module acquires the device information, and the second moment is the moment when the face image acquisition module acquires the face image.

4. The system according to claim 1, wherein, in terms of determining a set of collision probabilities based on each set of collision counts to obtain M sets of collision probabilities, the electronic device is specifically configured to: determine a total number of collisions based on each set of collision counts to obtain M total numbers of collisions. The j-th total number of collisions is used to represent the total number of collisions between the j-th device identifier and the N user identifiers; determine a set of collision probabilities based on the i-th collision count included in each set of collision counts and the total number of collisions corresponding to each set of collision counts to obtain M sets of collision probabilities, where 1 ≤ i ≤ N.

5. The system according to claim 1, wherein, in terms of determining N inverse collision probabilities based on the M sets of collision counts, the electronic device is specifically configured to: determine N quantities based on the M sets of collision counts. The N quantities correspond one-to-one to the N user identifiers. The i-th quantity is used to represent the number of device identifiers that collide with the i-th user identifier; determine N inverse collision probabilities based on the N quantities and the M. The i-th inverse collision probability is used to represent the probability that the M device identifiers collide with the i-th user identifier.

6. The system according to claim 1, wherein, in terms of determining M sets of probabilities based on the M sets of collision probabilities and the N inverse collision probabilities, the electronic device is specifically configured to: determine a set of probabilities based on the i-th collision probability included in each set of collision probabilities and the i-th inverse collision probability among the N inverse collision probabilities to obtain M sets of probabilities. The i-th probability value in the j-th set of probabilities is the product of the i-th collision probability in the j-th set of collision probabilities and the i-th inverse collision probability of the N inverse collision probabilities. The i-th probability value in the j-th set of probabilities is used to indicate the matching relationship between the j-th device identifier and the i-th user identifier.

7. The system according to any one of claims 1-6, wherein, in terms of determining interference identifiers based on the M sets of probabilities, the electronic device is specifically configured to: determine target probability values lower than a preset threshold in each set of probabilities; determine the user identifiers corresponding to the target probability values as interference identifiers.

8. An interference detection device, wherein, applied to an electronic device in an interference detection system, the device includes: An acquisition unit, configured to obtain M sets of collision times based on device information and face images collected by multiple information collection devices, where the M sets of collision times correspond one-to-one to M device identifiers, and the j-th set of collision times includes the number of collisions between the j-th device identifier and N user identifiers respectively, where both M and N are positive integers, and 1 ≤ j ≤ M; A first determination unit, configured to determine M probability sets based on the M sets of collision times, where the M device identifiers correspond one-to-one to the M probability sets; including: determining a set of collision probabilities based on each set of collision times to obtain M sets of collision probabilities; determining N inverse collision probabilities based on the M sets of collision times, where the N inverse collision probabilities correspond one-to-one to the N user identifiers; determining M probability sets based on the M sets of collision probabilities and the N inverse collision probabilities; A second determination unit, configured to determine an interference identifier based on the M probability sets, where the interference identifier is included in the N user identifiers.

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