Network access management system based on FPGA

Abstract

The invention discloses a network access management system based on an FPGA (Field Programmable Gate Array), which relates to the field of network access and comprises an access risk value calculation module, an access control request module, an access control module and a tracking chain processing module. An access control request module forms a collaborative multi-target self-adaptive access control request mechanism, the access control module performs hierarchical design of a basic access control mathematical model, mainly comprises an edge layer, an access calculation layer and a multi-dimensional control layer, and is combined with parallel network communication to realize multi-target access control. The problem that different access control directions and range differences in different hierarchies are large is solved from the five aspects of tasks, roles, attributes, risks and encryption, access content is controlled through an encryption algorithm, control over privacy risks is enhanced, meanwhile, the encryption algorithm is designed on the basis of an FPGA development board, a model is readjusted and optimized according to actual conditions, and the security of the system is improved. The privacy risk self-adaptive access control is improved, and the network access security is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of network access, in particular to a network access management system based on FPGA. Background Art

[0002] Network access refers to the process of users connecting to remote servers, websites or other network devices through networks such as the Internet or local area networks. During the network access process, users can communicate and exchange data with the network through various terminal devices (such as computers, mobile phones, tablets, etc.); network access usually requires identity authentication and authorization to ensure that only authorized users can access specific network resources. Common network access control methods include username and password verification, two-factor authentication, access control lists (ACLs), etc.

[0003] However, in the existing technology, with the development and improvement of technologies such as the Internet of Things, cloud computing, and big data, it has provided convenience for people's production and life, but it has also made the network environment more open, complex, and uncontrollable, posing security risks to the maintenance of user data and privacy. At the same time, due to the large network coverage, the roles and attributes set by users and systems are also very difficult to identify and discover, which to a certain extent increases the difficulty of controlling privacy risks and affects network access security. Summary of the invention

[0004] The purpose of the present invention is to provide a network access management system based on FPGA to solve the problem that the network environment proposed by the above background technology is more open, complicated and uncontrollable, which poses a security risk to the maintenance of user data and privacy. At the same time, due to the large network coverage, the roles and attributes set by users and systems are also very difficult to identify and discover, which to a certain extent increases the difficulty of controlling privacy risks and affects the problem of network access security.

[0005] To achieve the above object, the present invention provides the following technical solution: a network access management system based on FPGA, characterized in that it includes: an access risk value calculation module, an access control request module, an access control module and a tracking chain processing module;

[0006] The access control request module uses natural transformation or machine learning to label information data in the initial access control structure, sets the access control stage, and sets the actual access request in the standard to form a collaborative multi-objective adaptive access control request mechanism;

[0007] The access control module is constructed based on the access control request module and combined with parallel network communication technology. The basic access control mathematical model hierarchy is designed first, mainly including the edge layer, access calculation layer and multi-dimensional control layer. Different levels have different access control directions and scopes. Among the above levels, combined with parallel network communication, the supporting control units of the structure are task access control unit, role access control unit, attribute access control unit, risk access control unit and encryption access control unit.

[0008] The task access control unit formulates the goals and contents of basic privacy risk control and access; the role access control unit identifies the basic control objects; the attribute access control unit screens the identifiable types of current risks through parallel network communication technology; the risk access control unit is used for multi-risk positioning, risk control and risk analysis; the encryption access control unit relies on encryption algorithms to encrypt images;

[0009] The encryption algorithm is designed based on the FPGA development board and is divided into three steps:

[0010] S1, obtain the system initial value from the plaintext image through the SHA-256 hash function, and substitute the initial value and parameters into the system to obtain the chaotic sequence;

[0011] S2, using a chaotic sequence to scramble the index order of the pixels of the original image to obtain a scrambled image;

[0012] S3. Use chaotic sequence to perform layered XOR diffusion on the scrambled image to achieve image encryption.

[0013] Preferably, in step S1, a binary vector K is obtained from the plaintext image using the SHA-256 hash function to obtain a i , the formula is as follows:

[0014] a i =K(5(i-1)+1:5(i-1)+40),i=1,2,3,5,6,7,8,9,10;

[0015] Where: k = (x:y) represents the distribution of elements from x to y in vector K;

[0016] After algebraic transformation, three sets of initial values ​​of chaotic systems are generated, as follows:

[0017]

[0018] Where: bisde(a i ) represents the binary number a i Convert to decimal number;

[0019] The chaotic mapping is proposed as follows:

[0020]

[0021] In the formula, k, a are control parameters; x, y, z are state variables;

[0022] Set the control parameters k = 10, a = 10, and give three sets of initial values ​​(x1, y5, z5), (x2, y6, z9), (x3, y7, z 10 ), and set the initial value and parameter as the system key. Under the iteration of the above formula, three groups of chaotic sequences with M×N+2000 elements will be generated. M and N are the sizes of the color plaintext image P, and there are P R , P G , P B Three layers, each layer has M×N pixels, and all the pixels of the plaintext image are regarded as an array, that is:

[0023]

[0024] In the formula, p is the set of these pixels;

[0025] Discard the first 2000 elements and obtain three chaotic sequences X, Y, and Z, as follows:

[0026]

[0027] The sequence H is obtained by averaging, namely:

[0028]

[0029] Preferably, in step S2, each pseudo-random number in the chaotic sequence X, Y, Z is compared with the plaintext image P R , P G , P B Each pixel is indexed in order, namely:

[0030]

[0031] Arrange the chaotic sequences X, Y, and Z in ascending order, as shown in the following formula:

[0032]

[0033] Where: X up is the result of chaotic sequence X in ascending order; x low and x high Represent the lowest and highest values ​​in sequence X respectively; y low ,y high , z low , zhigh Similarly;

[0034] After that, each pixel of the plaintext image is subjected to synchronous index transformation with the chaotic sequence to obtain the scrambled pixel, as shown in the formula:

[0035]

[0036] Where: P * It is the result of synchronous index transformation between the pixels in the plaintext image and the chaotic sequence.

[0037] Preferably, in the step S3, the chaotic sequence X, Y, Z, H is processed and the sequence X, Y, Z, H is transformed into an integer in the range of 0 to 255, which is denoted as X * , Y * , Z * , H * :

[0038]

[0039] Using the chaotic sequence H * right Perform an XOR operation on the pixels in the * , Y * , Z * Perform a second XOR operation and obtain the final encrypted image C through layered diffusion, that is:

[0040]

[0041] Preferably, the decryption algorithm is the inverse process of the encryption algorithm, and the steps are reversed.

[0042] Preferably, the access control module divides the current privacy risk access control into multiple links, and establishes an adaptive access control mathematical calculation matrix therein to calculate the current access control frequency, as shown in the formula:

[0043] M=a×i×λ×v×R isk

[0044] Where: a represents the unit access distance, i represents the access controllable difference, λ represents the expected control reference value, v represents the conversion ratio, R isk represents the periodic risk value;

[0045] According to the actual situation, the obtained access control frequency is set as the processing standard of the upper limit access control model. Combined with parallel network communication technology, the current structure is adjusted and optimized. According to the access control requirements of the mathematical model, the expected standard value of privacy risk access control needs to be calculated, such as:

[0046]

[0047] Where: J represents the expected standard value of privacy risk access control, κ represents the access range, D represents the stage control constant, π1 and η2 represent the initial privacy risk value and the actual privacy risk value respectively, t represents the number of visits, and η represents the conversion difference;

[0048] The expected standard value is set in the access control mathematical model to form the basic control conditions and standard framework. As the coverage of network usage increases, the transmission and sharing of daily data volume is also more controlled and managed. Using parallel network communication technology, the same type of data and information are converted into formats, and transmitted and shared at fixed points through the initially set parallel lines. The process is controlled in stages in an adaptive manner to form a cyclical mathematical model structure to achieve the initial access control purpose.

[0049] Preferably, the access risk value calculation module sets a corresponding privacy risk access control cycle and calculates the risk value of each cycle. Usually, the initial model records the user's access behavior and summarizes the corresponding risk rules, sets a matching access tag according to the access subject, measures the current access control data volume, and calculates the periodic risk value under different access volume backgrounds, such as the formula:

[0050]

[0051] Where: H j (a i ) and H(a i ) represent the utility mean and utility function respectively;

[0052] According to the actual situation, the initial risk value is calculated, the basic risk standards and conditions are set, and the privacy risk access control mathematical model design environment is constructed.

[0053] Preferably, the tracking chain processing module integrates the risk collaborative control and tracking positioning form set for access records, access data, and access targets with the constructed access control module to establish a tracking chain.

[0054] Preferably, the tracking chain is used to determine and calculate the average risk of the current access user, and explore the fluctuation range of the adaptive access control risk value under different time periods. In this environment, the judgment function of the tracking chain tolerance is calculated, such as:

[0055]

[0056] Where U e represents the judgment function of the tracking chain tolerance, w rrepresents the tracking range, and θ represents the tolerance fluctuation range;

[0057] The resulting judgment function is used as a criterion for determining and tracking privacy risks, and is integrated with the access control module to form the final risk tag and analysis, completing the construction of the model, thereby achieving the purpose of privacy risk adaptive access control.

[0058] Preferably, the access risk value calculation module, the access control request module, the access control module and the tracking chain processing module are electrically connected in sequence.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. In the present invention, the access control request module forms a collaborative multi-objective adaptive access control request mechanism. The access control module designs the basic access control mathematical model hierarchy, which mainly includes the edge layer, the access calculation layer and the multi-dimensional control layer. Combined with parallel network communication, it solves the problems of different access control directions and large differences in scope in different levels from five aspects: tasks, roles, attributes, risks and encryption. At the same time, an encryption algorithm is designed based on the FPGA development board, and the model is readjusted and optimized according to actual conditions to improve adaptive access control to privacy risks and improve network access security.

[0061] 2. In the present invention, the goals and contents of basic privacy risk control and access are formulated, the basic control objects are identified, the identifiable types of current risks are screened, multiple risks are located, risks are controlled, and risks are analyzed. Encryption algorithms are used to control access content, strengthen control over privacy risks, and improve network access security. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flowchart of a network access management system based on FPGA according to the present invention;

[0063] Figure 2 A flowchart of a specific implementation of an access control module in an FPGA-based network access management system of the present invention;

[0064] Figure 3 The present invention is a flowchart of an encryption algorithm of an access control module in an FPGA-based network access management system.

[0065] In the figure: 1. Access risk value calculation module; 2. Access control request module; 3. Access control module; 31. Task access control unit; 32. Role access control unit; 33. Attribute access control unit; 34. Risk access control unit; 35. Encryption access control unit; 4. Tracking chain processing module. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Example

[0068] Reference Figure 1-3 As shown: A network access management system based on FPGA, characterized in that it includes: an access risk value calculation module 1, an access control request module 2, an access control module 3 and a tracking chain processing module 4, and the access risk value calculation module 1, the access control request module 2, the access control module 3 and the tracking chain processing module 4 are electrically connected in sequence;

[0069] The access risk value calculation module 1 sets the corresponding privacy risk access control cycle and calculates the risk value of each cycle. Usually, the initial model will record the user's access behavior and summarize the corresponding risk rules. It will set the matching access label according to the access subject, measure the current access control data volume, and calculate the periodic risk value under different access volume backgrounds, such as:

[0070]

[0071] Where: H j (a i ) and H(a i ) represent the utility mean and utility function respectively;

[0072] According to the actual situation, the initial risk value is calculated, the basic risk standards and conditions are set, and the privacy risk access control mathematical model design environment is constructed;

[0073] In the initial access control structure, the access control request module 2 uses natural transformation or machine learning to label the information data, sets the access control stage, and sets the actual access request in the standard to form a collaborative multi-objective adaptive access control request mechanism. Based on the needs of adaptive access control, the control indicators and specific values ​​of the actual request mechanism are adjusted, as shown in the table:

[0074] Access control request mechanism indicators and value table

[0075]

[0076]

[0077] Guided by the initial access control request, the corresponding model execution target is formulated. A unified access control relationship needs to be established between the currently set targets, and rational control should also be performed during the execution process to avoid problems such as model task execution deviation or error.

[0078] The access control module 3 divides the current privacy risk access control into multiple links, and establishes an adaptive access control mathematical calculation matrix among them to calculate the current access control frequency, as shown in the formula:

[0079] M=a×i×λ×v×R isk

[0080] Where: a represents the unit access distance, i represents the access controllable difference, λ represents the expected control reference value, ν represents the conversion ratio, R isk represents the periodic risk value;

[0081] According to the actual situation, the obtained access control frequency is set as the processing standard of the upper limit access control model. Combined with parallel network communication technology, the current structure is adjusted and optimized. According to the access control requirements of the mathematical model, the expected standard value of privacy risk access control needs to be calculated, such as:

[0082]

[0083] Where: J represents the expected standard value of privacy risk access control, κ represents the access range, D represents the stage control constant, π1 and π2 represent the initial privacy risk value and the actual privacy risk value respectively, t represents the number of accesses, and η represents the conversion difference;

[0084] The expected standard value is set in the access control mathematical model to form the basic control conditions and standard framework. As the coverage of network usage increases, the transmission and sharing of daily data volume is also more controlled and managed. Using parallel network communication technology, the same type of data and information are converted into formats, and transmitted and shared at fixed points through the initially set parallel lines. The process is controlled in stages in an adaptive manner to form a cyclical mathematical model structure to achieve the initial access control purpose;

[0085] The access control module 3 is constructed based on the access control request module 2 and combined with parallel network communication technology. The basic access control mathematical model hierarchy is designed first, mainly including the edge layer, access calculation layer and multi-dimensional control layer. Different levels have different access control directions and scopes. Among the above levels, combined with parallel network communication, the supporting control units of the structure are task access control unit 31, role access control unit 32, attribute access control unit 33, risk access control unit 34 and encryption access control unit 35.

[0086] The task access control unit 31 formulates the goals and contents of basic privacy risk control and access; the role access control unit 32 identifies the basic control objects; the attribute access control unit 33 screens the identifiable types of current risks through parallel network communication technology; the risk access control unit 34 is used for multi-risk positioning, risk control and risk analysis; the encryption access control unit 35 relies on encryption algorithms to encrypt images;

[0087] The encryption algorithm is designed based on the FPGA development board and is divided into three steps:

[0088] S1, obtain the system initial value from the plaintext image through the SHA-256 hash function, and substitute the initial value and parameters into the system to obtain the chaotic sequence;

[0089] Specifically, the SHA-256 hash function is used to obtain the binary vector K from the plaintext image to obtain a i , the formula is as follows:

[0090] a i =k(5(i-1)+1:5(i-1)+40),i=1,2,3,5,6,7,8,9,10;

[0091] Where: k = (x:y) represents the distribution of elements from x to y in vector K;

[0092] After algebraic transformation, three sets of initial values ​​of chaotic systems are generated, as follows:

[0093]

[0094] Where: bisde(a i ) represents the binary number a i Convert to decimal number;

[0095] The chaotic mapping is proposed as follows:

[0096]

[0097] In the formula, k, a are control parameters; x, y, z are state variables;

[0098] Set the control parameters k = 10, a = 10, and give three sets of initial values ​​(x1, y5, z5), (x2, y6, z9), x3, y7, z 10 ), and set the initial value and parameter as the system key. Under the iteration of the above formula, three groups of chaotic sequences with M×N+2000 elements will be generated. M and N are the sizes of the color plaintext image P, and there are P R , P G , P B Three layers, each layer has M×N pixels, and all the pixels of the plaintext image are regarded as an array, that is:

[0099]

[0100] In the formula, p is the set of these pixels;

[0101] Discard the first 2000 elements and obtain three chaotic sequences X, Y, and Z, as follows:

[0102]

[0103] The sequence H is obtained by averaging, namely:

[0104]

[0105] S2, using a chaotic sequence to scramble the index order of the pixels of the original image to obtain a scrambled image;

[0106] Specifically, each pseudo-random number in the chaotic sequence X, Y, Z is compared with the plaintext image P R , P G , P B Each pixel is indexed in order, namely:

[0107]

[0108] Arrange the chaotic sequences X, Y, and Z in ascending order, as shown in the following formula:

[0109]

[0110] Where: X up is the result of chaotic sequence X in ascending order; x low and x high Represent the lowest and highest values ​​in sequence X respectively; y low ,y high , z low , z high Similarly;

[0111] After that, each pixel of the plaintext image is subjected to synchronous index transformation with the chaotic sequence to obtain the scrambled pixel, as shown in the formula:

[0112]

[0113] Where: P * It is the result of synchronous index transformation between the pixels in the plaintext image and the chaotic sequence.

[0114] S3, using chaotic sequence to perform layered XOR diffusion on the scrambled image to achieve image encryption;

[0115] Specifically, the chaotic sequence X, Y, Z, H is processed and the sequence X, Y, Z, H is transformed into an integer in the range of 0 to 255, denoted as X * , Y * , Z * , H * :

[0116]

[0117] Using the chaotic sequence H * right Perform an XOR operation on the pixels in the * , Y * , Z * Perform a second XOR operation and obtain the final encrypted image C through layered diffusion, that is:

[0118]

[0119] The decryption algorithm is the reverse process of the encryption algorithm, and the steps can be reversed.

[0120] The tracking chain processing module 4 integrates the access control module 3 with the risk collaborative control and tracking positioning of the access records, access data, and access target settings to establish a tracking chain;

[0121] Use the tracking chain to determine and calculate the average risk of the current access user, and explore the fluctuation range of the adaptive access control risk value under different time periods. In this environment, calculate the judgment function of the tracking chain tolerance, as shown in the formula:

[0122]

[0123] Where U e represents the judgment function of the tracking chain tolerance, w r represents the tracking range, and θ represents the tolerance fluctuation range;

[0124] The obtained judgment function is used as the judgment and tracking standard for privacy risks and integrated with the access control module 3 to form the final risk labeling and analysis, complete the construction of the model, and thus achieve the purpose of privacy risk adaptive access control.

[0125] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A network access management system based on FPGA, characterized in that: include: Access risk value calculation module (1), access control request module (2), access control module (3) and tracking chain processing module (4); The access control request module (2) uses natural transformation or machine learning to label information data in the initial access control structure, sets the access control stage, and sets the actual access request in the standard to form a collaborative multi-objective adaptive access control request mechanism; The access control module (3) is constructed based on the access control request module (2) in combination with parallel network communication technology, and the supporting control units of the construction structure are task access control unit (31), role access control unit (32), attribute access control unit (33), risk access control unit (34) and encryption access control unit (35); The task access control unit (31) formulates the objectives and contents of basic privacy risk control and access; the role access control unit (32) identifies the basic control object; the attribute access control unit (33) screens the identifiable types of current risks through parallel network communication technology; the risk access control unit (34) is used for multi-risk positioning, risk control and risk analysis; the encryption access control unit (35) relies on encryption algorithm to realize image encryption; The encryption algorithm is designed based on the FPGA development board and is divided into three steps: S1, obtain the system initial value from the plaintext image through the SHA-256 hash function, and substitute the initial value and parameters into the system to obtain the chaotic sequence; S2, using a chaotic sequence to scramble the index order of the pixels of the original image to obtain a scrambled image; S3. Use chaotic sequence to perform layered XOR diffusion on the scrambled image to achieve image encryption.

2. The FPGA-based network access management system according to claim 1, characterized in that: In step S1, the binary vector K is obtained from the plaintext image using the SHA-256 hash function to obtain a i , the formula is as follows: a i =K(5(i-1)+1:5(i-1)+40),i=1,2,3,5,6,7,8,9,10; Where: k = (x:y) represents the distribution of elements from x to y in vector K; After algebraic transformation, three sets of initial values ​​of chaotic systems are generated, as follows: Where: bisde(a i ) represents the binary number a i Convert to decimal number; The chaotic mapping is proposed as follows: In the formula, k, a are control parameters; x, y, z are state variables; Set the control parameters k = 10, a = 10, and give three sets of initial values ​​(x1, y5, z5), (x2, y6, z9), (x3, y7, z 10 ), and set the initial value and parameter as the system key. Under the iteration of the above formula, three groups of chaotic sequences with M×N+2000 elements will be generated. M and N are the sizes of the color plaintext image P, and there are P R , P G , P B Three layers, each layer has M×N pixels, and all the pixels of the plaintext image are regarded as an array, that is: In the formula, p is the set of these pixels; Discard the first 2000 elements and obtain three chaotic sequences X, Y, and Z, as follows: The sequence H is obtained by averaging, namely:

3. The FPGA-based network access management system according to claim 1, characterized in that: In step S2, each pseudo-random number in the chaotic sequence X, Y, Z is compared with the plaintext image P R , P G , P B Each pixel is indexed in order, namely: Arrange the chaotic sequences X, Y, and Z in ascending order, as shown in the following formula: Where: X up is the result of chaotic sequence X in ascending order; x low and x high Represent the lowest and highest values ​​in sequence X respectively; y low ,y high , z low , z high Similarly; After that, each pixel of the plaintext image is subjected to synchronous index transformation with the chaotic sequence to obtain the scrambled pixel, as shown in the formula: Where: P * It is the result of synchronous index transformation between the pixels in the plaintext image and the chaotic sequence.

4. The FPGA-based network access management system according to claim 1, characterized in that: In the step S3, the chaotic sequence X, Y, Z, H is processed and the sequence X, Y, Z, H is transformed into an integer in the range of 0 to 255, which is denoted as X * , Y * , Z * , H * : Using the chaotic sequence H * P R* , P G* , P B* Perform an XOR operation on the pixels in the * , Y * , Z * Perform a second XOR operation and obtain the final encrypted image C through layered diffusion, that is:

5. The FPGA-based network access management system according to claim 4, characterized in that: The decryption algorithm is the reverse process of the encryption algorithm, and the steps can be reversed.

6. The FPGA-based network access management system according to claim 1, characterized in that: The access control module (3) divides the current privacy risk access control into multiple links, and establishes an adaptive access control mathematical calculation matrix therein to calculate the current access control frequency, as shown in the formula: M=a×i×λ×v×R isk Where: a represents the unit access distance, i represents the access controllable difference, λ represents the expected control reference value, ν represents the conversion ratio, R isk represents the periodic risk value; According to the access control requirements of the mathematical model, it is necessary to calculate the expected standard value of privacy risk access control, such as: Where: J represents the expected standard value of privacy risk access control, κ represents the access range, D represents the stage control constant, π1 and π2 represent the initial privacy risk value and the actual privacy risk value respectively, t represents the number of accesses, and η represents the conversion difference; The expected standard value is set in the access control mathematical model to form the basic control conditions and standard framework. The parallel network communication technology is used to convert the format of data and information of the same type, and the fixed-point transmission and sharing are carried out through the initially set parallel lines. The process is controlled in stages in an adaptive manner to form a cyclical mathematical model structure.

7. The FPGA-based network access management system according to claim 1, characterized in that: The access risk value calculation module (1) sets the corresponding privacy risk access control cycle and calculates the risk value of each cycle, as shown in the formula: Where: H j (a i ) and H(a i ) represent the utility mean and utility function respectively; According to the actual situation, the initial risk value is calculated, the basic risk standards and conditions are set, and the privacy risk access control mathematical model design environment is constructed.

8. The FPGA-based network access management system according to claim 1, characterized in that: The tracking chain processing module (4) is integrated with the constructed access control module (3) to establish a tracking chain for risk collaborative control and tracking positioning of access records, access data, and access target settings.

9. The FPGA-based network access management system according to claim 8, characterized in that: The tracking chain is used to determine and calculate the average risk of the current access user, and explore the fluctuation range of the adaptive access control risk value under different time periods. In this environment, the judgment function of the tracking chain tolerance is calculated, such as: Where U e represents the judgment function of the tracking chain tolerance, w r represents the tracking range, and θ represents the tolerance fluctuation range; The obtained judgment function is used as a criterion for determining and tracking privacy risks and integrated with the access control module (3) to form a final risk tag and analysis, thus completing the construction of the model.

10. The FPGA-based network access management system according to claim 1, characterized in that: The access risk value calculation module (1), the access control request module (2), the access control module (3) and the tracking chain processing module (4) are electrically connected in sequence.

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