A device fingerprint generation method, apparatus, device and readable storage medium

Abstract

The application relates to the technical field of Internet, in particular to a device fingerprint generation method and device, equipment and a readable storage medium. The method comprises the following steps: obtaining a client mark ID generated by a client random number generator and device information of a target device; judging whether a device fingerprint calculation module can normally run; if the device fingerprint calculation module is abnormal, the client mark ID is taken as the device fingerprint of the target device; if the device fingerprint calculation module can normally run, the device information of the target device is input into the device fingerprint calculation module for processing, so that a gray scale diagram corresponding to the device information of the target device is obtained; and the gray scale diagram corresponding to the device information and the device information of the target device are structurally spliced to obtain the device fingerprint of the target device. The method can improve the effectiveness and stability of the device fingerprint by combining the device information without sufficient expert experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet, in particular to a device fingerprint generation method and device, equipment and readable storage medium. BACKGROUND

[0002] The existing device fingerprint generation technology mostly relies on a stable feature as the main feature, such as IMEI, MAC address, etc., and combines a large amount of ordinary device information to generate an algorithm. This method relies heavily on relatively stable features, but as the mobile terminal privacy protection laws and regulations and systems are improved, some feature data cannot be easily obtained, and the selection of features and the allocation of feature weights rely heavily on technology. The current weight allocation scheme mostly relies on the experience of experts in each system, and the effect is uneven. SUMMARY

[0003] The purpose of the present application is to provide a device fingerprint generation method, device, equipment and readable storage medium to improve the above problems. In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0004] On the one hand, the present application provides a device fingerprint generation method, which comprises: obtaining a client mark ID generated by a client random number generator and device information of a target device; judging whether a device fingerprint calculation module can normally run, if the device fingerprint calculation module is abnormal, the client mark ID is taken as the device fingerprint of the target device; if the device fingerprint calculation module can normally run, the device information of the target device is input into the device fingerprint calculation module for processing to obtain a gray image corresponding to the device information of the target device; and the gray image corresponding to the device information and the device information of the target device are spliced to obtain the device fingerprint of the target device.

[0005] Optionally, the judgment of whether the device fingerprint calculation module can normally run, if the device fingerprint calculation module is abnormal, the client mark ID is taken as the device fingerprint of the target device, comprises:

[0006] Obtaining the running state and network connection of the device fingerprint calculation module, and judging whether the device fingerprint calculation module can normally run according to the running state and network connection of the device fingerprint calculation module;

[0007] If the device fingerprint calculation module has network and running abnormalities, the target device is continuously tracked through the client mark ID, and the client mark ID is taken as the device fingerprint of the target device.

[0008] Optionally, if the device fingerprint calculation module is normal, the device information of the target device is input into the device fingerprint calculation module for processing to obtain a gray scale image corresponding to the device information of the target device, comprising:

[0009] determining whether the device fingerprint calculation module is normal, if the device fingerprint calculation module is normal, extracting feature data N of the target device, mapping each of the feature data into a vector in F-dimensional space to generate N F-dimensional vectors;

[0010] converting each of the F-dimensional vectors into a byte array to obtain an N*M matrix, the N*M matrix as an N*M gray scale image.

[0011] Optionally, the device information corresponding to the gray scale image and the device information of the target device are structurally spliced to obtain the device fingerprint of the target device, comprising:

[0012] obtaining a device fingerprint splicing structure of the target device;

[0013] processing the gray scale image using a mean hash algorithm to obtain a hash average value of the gray scale image;

[0014] structurally splicing the hash average value of the gray scale image and the device information of the target device according to the device fingerprint splicing structure of the target device to obtain the device fingerprint of the target device.

[0015] Optionally, the device information corresponding to the gray scale image and the device information of the target device are structurally spliced to obtain the device fingerprint of the target device, further comprising:

[0016] sending the device fingerprint of the target device to a device fingerprint library for retrieval;

[0017] if no similar device is retrieved, storing and marking the device fingerprint of the target device;

[0018] if a similar device is retrieved, updating and marking the device fingerprint of the target device.

[0019] Optionally, the device information corresponding to the gray scale image and the device information of the target device are structurally spliced to obtain the device fingerprint of the target device, further comprising:

[0020] processing a plurality of devices to obtain a plurality of bit sequences, and calculating the plurality of bit sequences using a Hamming distance algorithm to obtain the similarity between the plurality of devices;

[0021] obtaining a first threshold value, the first threshold value being a device fingerprint similarity threshold value;

[0022] determining whether the similarity between each two of the devices is less than a similarity threshold value, and if the similarity between the plurality of the devices is less than the similarity threshold value, sending the two of the device information to a communication device of a manager and notifying the manager to manually determine whether the two of the devices are identical.

[0023] In a second aspect, an embodiment of the present application provides a device fingerprint generation apparatus, the apparatus comprising:

[0024] a first obtaining unit configured to obtain a client mark ID generated by a client random number generator and device information of a target device;

[0025] a first determining unit configured to determine whether a device fingerprint calculation module can normally operate, and if the device fingerprint calculation module is abnormal, taking the client mark ID as a device fingerprint of the target device;

[0026] a second determining unit configured to, if the device fingerprint calculation module can normally operate, input the device information of the target device into the device fingerprint calculation module for processing to obtain a gray scale image corresponding to the device information of the target device;

[0027] a first processing unit configured to perform structural splicing on the gray scale image corresponding to the device information and the device information of the target device to obtain a device fingerprint of the target device.

[0028] Optionally, the apparatus comprises:

[0029] a first obtaining subunit configured to obtain an operating state and a network connection state of the device fingerprint calculation module, and determine whether the device fingerprint calculation module can normally operate according to the operating state and the network connection state of the device fingerprint calculation module;

[0030] a first determining subunit configured to, if the device fingerprint calculation module is abnormal in network and operation, continuously track the target device through the client mark ID, and take the client mark ID as a device fingerprint of the target device.

[0031] Optionally, the apparatus comprises:

[0032] a second determining subunit configured to determine whether the device fingerprint calculation module normally operates, if the device fingerprint calculation module normally operates, extract N characteristic data of the target device, map each of the characteristic data into a vector in an F-dimensional space, and generate N F-dimensional vectors.

[0033] The first processing subunit is configured to convert each of the F-dimensional vectors into a byte array to obtain an N*M matrix, and the N*M matrix is used as an N*M grayscale image.

[0034] Optionally, the device comprises:

[0035] The second acquisition subunit is configured to acquire a device fingerprint splicing structure of the target device.

[0036] The second processing subunit is configured to process the grayscale image by using a mean hash algorithm to obtain a hash average value of the grayscale image.

[0037] The third processing subunit is configured to splicethe hash average value of the grayscale image and the device information of the target device according to the device fingerprint splicing structure of the target device to obtain a device fingerprint of the target device.

[0038] Optionally, the device further comprises:

[0039] The first sending unit is configured to send the device fingerprint of the target device to a device fingerprint library for retrieval.

[0040] The third judging unit is configured to, if no similar device is retrieved, store and mark the device fingerprint of the target device.

[0041] The fourth judging unit is configured to, if a similar device is retrieved, update and mark the device fingerprint of the target device.

[0042] Optionally, the device further comprises:

[0043] The second processing unit is configured to process a plurality of devices to obtain a plurality of bit sequences, and calculate the similarity between the plurality of devices by using a Hamming distance algorithm.

[0044] The second acquisition unit is configured to acquire a first threshold value, which is a device fingerprint similarity threshold value.

[0045] The fifth judging unit is configured to judge whether the similarity between each two of the devices is less than a similarity threshold value, and if the similarity between the plurality of devices is less than the similarity threshold value, send the two device information to a communication device of a management personnel and notify the management personnel to manually judge whether the two devices are the same.

[0046] In a third aspect, an embodiment of the present application provides a device fingerprint generation device, which comprises a memory and a processor. The memory is configured to store a computer program, and the processor is configured to execute the computer program to implement the steps of the device fingerprint generation method.

[0047] In a fourth aspect, an embodiment of the present application provides a readable storage medium, and the readable storage medium stores a computer program, and the computer program is executed by a processor to implement steps of the device fingerprint generation method.

[0048] The present application has the following beneficial effects:

[0049] 1. The method of the present application improves the effectiveness and stability of the device fingerprint by collecting non-sensitive device data and combining device information without sufficient expert experience.

[0050] 2. The method of the present application is optimized for short text scenarios with less device information, and can effectively obtain the device fingerprint and improve the retrieval ability of the device fingerprint by combining pre-generated data.

[0051] Other features and advantages of the present application will be described in the following description, and some will become apparent from the description, or will be understood from the practice of the present application. The purpose and other advantages of the present application can be achieved and obtained by the structure specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation to the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0053] Figure 1 A device fingerprint generation method flowchart described in an embodiment of the present application;

[0054] Figure 2 A device fingerprint generation device structure diagram described in an embodiment of the present application;

[0055] Figure 3 A device fingerprint generation device structure diagram described in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0057] It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are only used to distinguish description, and cannot be understood as indicating or implying relative importance.

[0058] As shown in the Figure 1 The present embodiment provides a device fingerprint generation method, and the method comprises steps S1, S2, S3 and S4.

[0059] Step S1, obtaining a client mark ID generated by a client random number generator and device information of a target device;

[0060] Step S2, judging whether a device fingerprint calculation module can normally run, if the device fingerprint calculation module is abnormal, taking the client mark ID as the device fingerprint of the target device;

[0061] Step S3, if the device fingerprint calculation module can normally run, inputting the device information of the target device into the device fingerprint calculation module for processing to obtain a gray scale image corresponding to the device information of the target device;

[0062] Step S4, structurally splicing the gray scale image corresponding to the device information and the device information of the target device to obtain the device fingerprint of the target device.

[0063] It can be understood that the present application is to improve the effectiveness and stability of the device fingerprint by collecting non-sensitive device data and combining device information without sufficient expert experience.

[0064] It can be understood that the present application is to obtain the device fingerprint by processing the device information and then structurally splicing the gray scale image corresponding to the device information and the device information of the target device.

[0065] In one specific embodiment of the present disclosure, the step S2 comprises a step S21 and a step S22.

[0066] The step S21 acquires the running state and network connection of the device fingerprint calculation module, and determines whether the device fingerprint calculation module can normally run according to the running state and network connection of the device fingerprint calculation module.

[0067] The step S22 continuously tracks the target device through the client mark ID if the device fingerprint calculation module has network and running abnormalities, and takes the client mark ID as the device fingerprint of the target device.

[0068] It can be understood that this step is a pre-processing for an emergency situation, and a backup plan is implemented when the device has abnormalities. The target device is continuously tracked through the client mark ID, and the client mark ID is taken as the device fingerprint of the target device.

[0069] In one specific embodiment of the present disclosure, the step S3 comprises a step S31 and a step S32.

[0070] The step S31 determines whether the device fingerprint calculation module normally runs. If the device fingerprint calculation module normally runs, the characteristic data of the target device is extracted, each characteristic data is mapped as a vector in an F-dimensional space, and N F-dimensional vectors are generated.

[0071] The step S32 converts each F-dimensional vector into a byte array to obtain an N*M matrix, and the N*M matrix is taken as an N*M grayscale image.

[0072] It can be understood that this step is to process the device fingerprint, extract the characteristic data of the target device, convert multiple characteristic data into vectors, and then establish a grayscale image to improve the stability and effectiveness of the device fingerprint.

[0073] In one specific embodiment of the present disclosure, the step S4 comprises a step S41, a step S42 and a step S43.

[0074] The step S41 acquires the device fingerprint splicing structure of the target device.

[0075] The step S42 processes the grayscale image by using a mean hash algorithm to obtain the hash average value of the grayscale image.

[0076] The step S43 splices the hash average value of the grayscale image and the device information of the target device according to the device fingerprint splicing structure of the target device to obtain the device fingerprint of the target device.

[0077] It can be understood that this step is to obtain a device fingerprint splicing structure by processing the device fingerprint, then calculate the hash average value of the gray image by the mean hash algorithm, and splice the hash average value of the gray image and the feature data in the device information, and then obtain the device fingerprint.

[0078] In one specific embodiment of the present disclosure, the step S4 is followed by steps S5, S6 and S7.

[0079] Step S5, the device fingerprint of the target device is sent to the device fingerprint library for retrieval;

[0080] Step S6, if no similar device is retrieved, the device fingerprint of the target device is stored and marked;

[0081] Step S7, if a similar device is retrieved, the device fingerprint of the target device is updated and marked.

[0082] It can be understood that this step is to retrieve the device fingerprint, determine whether there is the same fingerprint in the fingerprint library, and store or update the device fingerprint according to the retrieval result.

[0083] In one specific embodiment of the present disclosure, the step S4 is followed by steps S8, S9 and S10.

[0084] Step S8, a plurality of devices are processed to obtain a plurality of bit sequences, and the plurality of bit sequences are calculated by using the Hamming distance algorithm to obtain the similarity between the plurality of devices;

[0085] Step S9, a first threshold is obtained, and the first threshold is a device fingerprint similarity threshold;

[0086] Step S10, determine whether the similarity between each two of the devices is less than the similarity threshold, if the similarity between the plurality of devices is less than the similarity threshold, the two device information is sent to the communication device of the management personnel, and the management personnel is notified to manually determine whether the two devices are the same.

[0087] It can be understood that this step is to process the device information of a plurality of devices by the mean hash algorithm to obtain a plurality of bit sequences, and mark the device, then calculate by using the Hamming distance algorithm to obtain the similarity between the plurality of devices, and then determine whether the similarity is less than the first threshold, because the smaller the similarity, the greater the probability that the two devices are the same, and then manually confirm.

[0088] Embodiment 2

[0089] As Figure 2 shown, the embodiment provides a device fingerprint generation apparatus, which comprises a first acquisition unit 701, a first judgment unit 702, a second judgment unit 703 and a first processing unit 704.

[0090] The first acquisition unit 701 is configured to acquire a client mark ID generated by a client random number generator and device information of a target device.

[0091] The first judgment unit 702 is configured to judge whether a device fingerprint calculation module can normally run, and if the device fingerprint calculation module is abnormal, the client mark ID is taken as a device fingerprint of the target device.

[0092] The second judgment unit 703 is configured to, if the device fingerprint calculation module can normally run, input the device information of the target device to the device fingerprint calculation module for processing to obtain a gray scale map corresponding to the device information of the target device.

[0093] The first processing unit 704 is configured to structure splice the gray scale map corresponding to the device information and the device information of the target device to obtain a device fingerprint of the target device.

[0094] In one specific embodiment of the present disclosure, the first judgment unit 702 comprises a first acquisition sub-unit 7021 and a first judgment sub-unit 7022.

[0095] The first acquisition sub-unit 7021 is configured to acquire a running state and a network connection condition of the device fingerprint calculation module, and judge whether the device fingerprint calculation module can normally run according to the running state and the network connection condition of the device fingerprint calculation module.

[0096] The first judgment sub-unit 7022 is configured to, if the device fingerprint calculation module is abnormal in network and running, continuously track the target device through the client mark ID, and take the client mark ID as a device fingerprint of the target device.

[0097] In one specific embodiment of the present disclosure, the second judgment unit 703 comprises a second judgment sub-unit 7031 and a first processing sub-unit 7032.

[0098] The second judgment sub-unit 7031 is configured to judge whether the device fingerprint calculation module normally runs, if the device fingerprint calculation module normally runs, extract N characteristic data of the target device, map each of the characteristic data into a vector in an F-dimensional space, and generate N F-dimensional vectors.

[0099] The first processing subunit 7032 is configured to convert each of the F-dimensional vectors into a byte array to obtain an N*M matrix, and the N*M matrix is used as an N*M grayscale image.

[0100] In an embodiment of the present disclosure, the first processing unit 704 includes a second acquisition subunit 7041, a second processing subunit 7042, and a third processing subunit 7043.

[0101] The second acquisition subunit 7041 is configured to acquire a device fingerprint splicing structure of the target device.

[0102] The second processing subunit 7042 is configured to process the grayscale image by using a mean hash algorithm to obtain a hash average value of the grayscale image.

[0103] The third processing subunit 7043 is configured to splice the hash average value of the grayscale image and the device information of the target device according to the device fingerprint splicing structure of the target device to obtain a device fingerprint of the target device.

[0104] In an embodiment of the present disclosure, the device further includes a first sending unit 705, a third judgment unit 706, and a fourth judgment unit 707.

[0105] The first sending unit 705 is configured to send the device fingerprint of the target device to a device fingerprint library for retrieval.

[0106] The third judgment unit 706 is configured to, if no similar device is retrieved, store and mark the device fingerprint of the target device.

[0107] The fourth judgment unit 707 is configured to, if a similar device is retrieved, update and mark the device fingerprint of the target device.

[0108] In an embodiment of the present disclosure, the device further includes a second processing unit 708, a second acquisition unit 709, and a fifth judgment unit 710.

[0109] The second processing unit 708 is configured to process a plurality of devices to obtain a plurality of bit sequences, and calculate the similarity between the plurality of devices by using a Hamming distance algorithm.

[0110] The second acquisition unit 709 is configured to acquire a first threshold value, and the first threshold value is a device fingerprint similarity threshold value.

[0111] The fifth judgment unit 710 is used to determine whether the similarity between any two devices is less than a similarity threshold. If the similarity between the multiple devices is less than the similarity threshold, the information of the two devices is sent to the communication device of the administrator, and the administrator is notified to manually determine whether the two devices are the same.

[0112] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0113] Example 3

[0114] Corresponding to the above method embodiments, this disclosure also provides a device fingerprint generation device. The device fingerprint generation device described below and the device fingerprint generation method described above can be referred to in correspondence:

[0115] Figure 3 This is a block diagram illustrating a device fingerprint generation device 800 according to an exemplary embodiment. For example... Figure 3 As shown, the fingerprint generation device 800 may include a processor 801 and a memory 802. The fingerprint generation device 800 may also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805.

[0116] The processor 801 is configured to control overall operations of the device fingerprint generation device 800 to complete all or part of the steps of the device fingerprint generation method described above. The memory 802 is configured to store various types of data to support operations of the device fingerprint generation device 800, which can include, for example, instructions for any application or method operating on the device fingerprint generation device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, and the like. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 803 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is configured to output and / or input audio signals. For example, the audio component can include a microphone configured to receive external audio signals. The received audio signals can be further stored in the memory 802 or transmitted through the communication component 805. The audio component also includes at least one speaker configured to output audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which can be a keyboard, a mouse, a button, and the like. The buttons can be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the device fingerprint generation device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include a Wi-Fi module, a Bluetooth module, an NFC module.

[0117] In an example embodiment, the device fingerprint generation device 800 can be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic elements for executing the above-mentioned device fingerprint generation method.

[0118] In another example embodiment, a computer readable storage medium including program instructions is also provided, which when executed by a processor, implement the steps of the above-mentioned device fingerprint generation method. For example, the computer readable storage medium can be the above-mentioned memory 802 including program instructions, which can be executed by the processor 801 of the device fingerprint generation device 800 to complete the above-mentioned device fingerprint generation method.

[0119] Embodiment 4

[0120] Corresponding to the above method embodiments, the embodiments of the present disclosure also provide a readable storage medium, and the readable storage medium described below can be referred to each other corresponding to the above-mentioned device fingerprint generation method.

[0121] A readable storage medium, the readable storage medium has a computer program stored thereon, and the computer program, when executed by a processor, implements the steps of the device fingerprint generation method of the above-mentioned method embodiments.

[0122] The readable storage medium can be specifically a U disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various readable storage media that can store program codes.

[0123] The above only describes the preferred embodiments of the present disclosure and is not used to limit the present disclosure. For those skilled in the art, the present disclosure can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

[0124] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A device fingerprint generation method, comprising: The method comprises the following steps: obtaining a client mark ID generated by a client random number generator and device information of a target device; determining whether a device fingerprint calculation module can normally operate, and if the device fingerprint calculation module is abnormal, taking the client mark ID as a device fingerprint of the target device; if the device fingerprint calculation module can normally operate, inputting the device information of the target device into the device fingerprint calculation module for processing to obtain a gray scale image corresponding to the device information of the target device; performing structural splicing on the gray scale image corresponding to the device information and the device information of the target device to obtain a device fingerprint of the target device; wherein, if the device fingerprint calculation module can normally operate, inputting the device information of the target device into the device fingerprint calculation module for processing to obtain a gray scale image corresponding to the device information of the target device, comprising: determining whether the device fingerprint calculation module is normally operating, and if the device fingerprint calculation module is normally operating, extracting N characteristic data of the target device, mapping each characteristic data into a vector in an F-dimensional space to generate N F-dimensional vectors; converting each F-dimensional vector into a byte array to obtain an N*M matrix, and taking the N*M matrix as an N*M gray scale image; the structural splicing of the gray scale image corresponding to the device information and the device information of the target device to obtain the device fingerprint of the target device, comprising: obtaining a device fingerprint splicing structure of the target device; processing the gray scale image by using a mean hash algorithm to obtain a hash average value of the gray scale image; structurally splicing the hash average value of the gray scale image and the device information of the target device according to the device fingerprint splicing structure of the target device to obtain the device fingerprint of the target device.

2. The device fingerprint generation method of claim 1, wherein, the determination of whether the device fingerprint calculation module can normally operate, and if the device fingerprint calculation module is abnormal, taking the client mark ID as a device fingerprint of the target device, comprising: obtaining a running state and a network connection condition of the device fingerprint calculation module, and determining whether the device fingerprint calculation module can normally operate according to the running state and the network connection condition of the device fingerprint calculation module; if the device fingerprint calculation module has network abnormality and operation abnormality, continuously tracking the target device through the client mark ID, and taking the client mark ID as a device fingerprint of the target device.

3. The device fingerprint generation method of claim 1, wherein, the structural splicing of the gray scale image corresponding to the device information and the device information of the target device to obtain the device fingerprint of the target device, further comprising: sending the device fingerprint of the target device to a device fingerprint library for retrieval; if no similar device is retrieved, storing and marking the device fingerprint of the target device; if a similar device is retrieved, updating and marking the device fingerprint of the target device.

4. The device fingerprint generation method of claim 1, wherein, the structural splicing of the gray scale image corresponding to the device information and the device information of the target device to obtain the device fingerprint of the target device, further comprising: Processing a plurality of devices to obtain a plurality of bit sequences, and calculating the plurality of bit sequences by using a Hamming distance algorithm to obtain the similarity between the plurality of devices; Obtaining a first threshold, which is a device fingerprint similarity threshold; Determining whether the similarity between each two of the devices is less than the similarity threshold, and if the similarity between the plurality of devices is less than the similarity threshold, sending the information of the two devices to a communication device of a manager and notifying the manager to manually determine whether the two devices are the same.

5. An apparatus fingerprint generation device, comprising: Comprise: A first obtaining unit configured to obtain a client marker ID generated by a client random number generator and device information of a target device; A first determining unit configured to determine whether a device fingerprint calculation module can normally operate, and if the device fingerprint calculation module is abnormal, taking the client marker ID as a device fingerprint of the target device; A second determining unit configured to, if the device fingerprint calculation module can normally operate, input the device information of the target device into the device fingerprint calculation module for processing to obtain a grayscale image corresponding to the device information of the target device; A first processing unit configured to structure splice the grayscale image corresponding to the device information and the device information of the target device to obtain a device fingerprint of the target device; A second determining subunit configured to determine whether the device fingerprint calculation module is normally operating, and if the device fingerprint calculation module is normally operating, extracting N characteristic data of the target device, mapping each of the characteristic data into a vector in an F-dimensional space to generate N F-dimensional vectors; A first processing subunit configured to convert each of the F-dimensional vectors into a byte array to obtain an N*M matrix, and taking the N*M matrix as an N*M grayscale image; A second obtaining subunit configured to obtain a device fingerprint splicing structure of the target device; A second processing subunit configured to process the grayscale image by using a mean hash algorithm to obtain a hash average value of the grayscale image; A third processing subunit configured to structure splice the hash average value of the grayscale image and the device information of the target device according to the device fingerprint splicing structure of the target device to obtain a device fingerprint of the target device.

6. The device fingerprint generation apparatus of claim 5, wherein, The device comprises: A first obtaining subunit configured to obtain the running state and network connection status of the device fingerprint calculation module, and determine whether the device fingerprint calculation module can normally operate according to the running state and network connection status of the device fingerprint calculation module; A first determining subunit configured to, if the device fingerprint calculation module is abnormal in network and operation, continuously track the target device by using the client marker ID, and take the client marker ID as a device fingerprint of the target device.

7. The device fingerprint generation apparatus of claim 5, wherein, The device further comprises: A first sending unit configured to send the device fingerprint of the target device to a device fingerprint library for retrieval; A third determining unit configured to, if no similar device is retrieved, store and mark the device fingerprint of the target device. The fourth judging unit is configured to update the device fingerprint of the target device and mark the target device if similar devices are found.

8. The device fingerprint generation apparatus of claim 5, wherein, The device further comprises: The second processing unit is configured to process a plurality of devices to obtain a plurality of bit sequences, and calculate the similarity between the plurality of devices by using a Hamming distance algorithm. The second obtaining unit is configured to obtain a first threshold value, which is a device fingerprint similarity threshold value. The fifth judging unit is configured to judge whether the similarity between each two of the devices is less than the similarity threshold value, and if the similarity between the plurality of devices is less than the similarity threshold value, the information of the two devices is sent to the communication device of the manager and the manager is notified to manually judge whether the two devices are identical.

9. A device fingerprint generating device, characterized by The device further comprises: The memory is configured to store a computer program. The processor is configured to implement the steps of the device fingerprint generation method according to any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium characterized by: The readable storage medium has a computer program stored thereon, and the computer program is configured to implement the steps of the device fingerprint generation method according to any one of claims 1 to 4 when executed by the processor.

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