Communication product safety management system based on artificial intelligence

Abstract

The invention relates to the technical field of communication product security, in particular to a communication product security management system based on artificial intelligence, which is used for solving the problems of low response speed, low efficiency, poor monitoring accuracy and difficulty in prevention of a traditional security management method. The system comprises a state monitoring module, an information processing module, an abnormity judgment module, an abnormity warning module and a behavior monitoring module. The system adopts an artificial intelligence technology, can quickly and accurately identify potential security threats of communication products, improves the efficiency of security detection, can organize and analyze a large amount of data, improves the accuracy of security detection, and can timely give an abnormal alarm to the communication products in which abnormal conditions are found. The high efficiency and accuracy of safety response are improved, intelligent safety management is achieved, safety requirements of different users can be met, a safer and more reliable communication environment is provided for the users, communication safety and data safety are guaranteed, and the method has wide application prospects.

Description

Technical Field

[0001] The present invention relates to the field of communication product security technology, and in particular to a communication product security management system based on artificial intelligence. Background Art

[0002] Communication products refer to equipment and tools used to achieve information transmission and communication functions. With the continuous development of science and technology, the types of communication products are becoming increasingly rich, bringing great convenience to people's lives and work.

[0003] With the popularization of the Internet and the rapid development of mobile communication technology, communication products play an increasingly important role in people's lives and work. However, the security issues of communication products are becoming increasingly prominent, posing a serious threat to users' privacy and property safety. Traditional security management methods often rely on manual monitoring and post-processing, which have the disadvantages of slow response, low efficiency, poor monitoring accuracy, and difficulty in prevention. Therefore, it is of great significance to develop a communication product security management system based on artificial intelligence. Summary of the invention

[0004] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a communication product security management system based on artificial intelligence, which solves the problems of slow response speed, low efficiency, poor monitoring accuracy and difficulty in prevention in traditional security management methods.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A communication product security management system based on artificial intelligence, comprising: A behavior monitoring module, used to obtain behavior monitoring parameters of the monitored object and send the behavior monitoring parameters to the information processing module; wherein the behavior monitoring parameters include data speed information SS, data deviation information SP and vulnerability information LD; An information processing module, used for obtaining a behavior monitoring coefficient XW according to the behavior monitoring parameter, and sending the behavior monitoring coefficient XW to the abnormality discrimination module; The specific process of the information processing module obtaining the behavior monitoring coefficient XW is as follows: The data rate information SS, the data deviation information SP and the vulnerability information LD are normalized, and the values ​​of the data rate information SS, the data deviation information SP and the vulnerability information LD are quantified according to a preset behavior monitoring model to obtain a behavior monitoring coefficient XW; Among them, the behavior monitoring model is as follows: Where: λ is the preset error adjustment factor, and λ=1.125 is taken; Both π and e are mathematical constants; w1, w2 and w3 are the preset weight factors corresponding to the set data rate information SS, data bias information SP and vulnerability information LD respectively. w1, w2 and w3 satisfy w2>w3>w1>2.117, and w1=2.56, w2=4.11, w3=3.49; An abnormality identification module is used to generate an abnormal behavior instruction according to the behavior monitoring coefficient XW, and send the abnormal behavior instruction to the abnormal warning module; The abnormal warning module is used to sound an abnormal behavior alarm ring after receiving an abnormal behavior instruction.

[0006] As a further solution of the present invention: the specific process of the behavior monitoring module obtaining the behavior monitoring parameters is as follows: After receiving the behavior monitoring instruction, the data transmission rate of the monitored object per unit time and the preset standard rate are obtained, the difference between the two is obtained, and it is marked as data rate information SS; Obtain all data sent by the monitoring object per unit time, and mark it as data sent by the communication product, obtain the total number of bytes of data sent by the communication product and the total number of bytes of data received by the data receiving object of the communication product sending data, obtain the difference between the two, and mark it as the data transmission value SF, obtain all data received by the monitoring object per unit time, and mark it as data received by the communication product, obtain the total number of bytes of data received by the communication product and the total number of bytes of data sent by the data sending object of the communication product receiving data, obtain the difference between the two, and mark it as the data connection value SJ, quantify the data transmission value SF and the data connection value SJ, extract the values ​​of the data transmission value SF and the data connection value SJ, and substitute them into the formula for calculation, according to the formula Obtain the digital deviation information SP; wherein e is a mathematical constant, s1 and s2 are respectively the preset proportional coefficients corresponding to the set digital transmission value SF and the digital connection value SJ, s1 and s2 satisfy s1+s2=1, 0<s2<s1<1, and s1=0.59, s2=0.41; Get the number of vulnerabilities that appear in the monitoring object within the preset time before the current moment, and mark it as the newly added value XZ. Get the total number of unrepaired vulnerabilities in the monitoring object at the current moment, and mark it as the existing value XC. Quantify the newly added value XZ and the existing value XC, extract the values ​​of the newly added value XZ and the existing value XC, and substitute them into the formula for calculation. Get vulnerability information LD; where e and π are mathematical constants, x1 and x2 are preset proportional coefficients corresponding to the set new value XZ and the existing value XC, respectively, x1 and x2 satisfy x1+x2=1, 0<x1<x2<1, and x1=0.38 and x2=0.62; The data speed information SS, data deviation information SP and vulnerability information LD are sent to the information processing module.

[0007] As a further solution of the present invention: the specific process of the abnormal identification module generating the abnormal behavior instruction is as follows: The behavior monitoring coefficient XW is compared with the preset behavior monitoring threshold XWy, and the comparison results are as follows: If the behavior monitoring coefficient XW≥behavior monitoring threshold XWy, a behavior abnormality instruction is generated and sent to the abnormal warning module.

[0008] As a further solution of the present invention: a state monitoring module is used to obtain state monitoring parameters of the monitored object and send the state monitoring parameters to the information processing module; wherein the state monitoring parameters include temperature difference information WC, pressure difference information YC, flow difference information LC and operation information YX.

[0009] As a further solution of the present invention: the specific process of the state monitoring module obtaining the state monitoring parameters is as follows: Mark the communication products that are subject to security management monitoring as monitoring objects; Obtain the real-time temperature of the monitored object and the preset standard temperature, obtain the difference between the two, and mark it as temperature difference information WC; Get the real-time voltage of the monitored object, get the maximum real-time voltage and the minimum real-time voltage per unit time, get the difference between the two, and mark it as the extreme difference value JC, get the average value of the real-time voltage per unit time, and mark it as the average voltage value, get the difference between the average voltage value and the preset standard voltage, and mark it as the rated difference value EC, get the extreme difference value JC and the rated difference value EC for quantification, extract the values ​​of the extreme difference value JC and the rated difference value EC, and substitute them into the formula for calculation, according to the formula The pressure difference information YC is obtained; wherein c1 and c2 are preset proportional coefficients corresponding to the set extreme difference value JC and rated difference value EC, respectively, c1 and c2 satisfy c1+c2=1, 0<c2<c1<1, and c1=0.63 and c2=0.37; Obtain the real-time current of the monitored object, obtain the average value of the real-time current in a unit time, and mark it as the average current value, obtain the difference between the average current value and the preset standard current, and mark it as the current difference information LC; Obtain the total running time and total number of startups of the monitored object from the first use to the current time, and mark them as running time value YS and startup number value QC respectively, draw a diamond figure with the values ​​of running time value YS and startup number value QC as the diagonal lines of the diamond, obtain the area of ​​the diamond figure, and mark it as running information YX; The temperature difference information WC, the pressure difference information YC, the flow difference information LC and the operation information YX are sent to the information processing module.

[0010] As a further solution of the present invention: the information processing module is also used to obtain a state monitoring coefficient ZT according to the state monitoring parameters, and send the state monitoring coefficient ZT to the abnormality discrimination module.

[0011] As a further solution of the present invention: the specific process of the information processing module obtaining the state monitoring coefficient ZT is as follows: The temperature difference information WC, the pressure difference information YC, the flow difference information LC and the operation information YX are normalized, and the values ​​of the temperature difference information WC, the pressure difference information YC, the flow difference information LC and the operation information YX are quantified according to a preset state monitoring model to obtain a state monitoring coefficient ZT; The condition monitoring model is as follows: Where: κ is the preset error adjustment factor, taking κ=0.927; Both π and e are mathematical constants; z1, z2, z3 and z4 are preset weight factors corresponding to the set temperature difference information WC, pressure difference information YC, flow difference information LC and operation information YX respectively. z1, z2, z3 and z4 satisfy z1>z3>z2>z4>1.615, and z1=3.51, z2=2.38, z3=2.79, z4=1.93; The state monitoring coefficient ZT is sent to the abnormality discrimination module.

[0012] As a further solution of the present invention: the abnormality identification module is also used to generate a state abnormality instruction or a behavior monitoring instruction according to the state monitoring coefficient ZT, and send the state abnormality instruction to the abnormal warning module and send the behavior monitoring instruction to the behavior monitoring module.

[0013] As a further solution of the present invention: the specific process of the abnormality identification module generating the state abnormality instruction or the behavior monitoring instruction is as follows: The condition monitoring coefficient ZT is compared with the preset condition monitoring threshold ZTy, and the comparison results are as follows: If the state monitoring coefficient ZT ≥ the state monitoring threshold ZTy, a state abnormality instruction is generated and sent to the abnormal warning module; If the state monitoring coefficient ZT<the state monitoring threshold ZTy, a behavior monitoring instruction is generated and sent to the behavior monitoring module.

[0014] As a further solution of the present invention: the abnormal warning module is also used to sound an abnormal status alarm ring after receiving an abnormal status instruction.

[0015] Beneficial effects of the present invention: The communication product safety management system based on artificial intelligence of the present invention first monitors and collects data on the operation status of the communication product to obtain the status monitoring parameters, then uses artificial intelligence technology to perform automatic data processing, and the status monitoring coefficient obtained according to the status monitoring parameters can comprehensively measure the abnormality of the operation status of the communication product, and the larger the status monitoring coefficient is, the higher the abnormality of the operation status is, which has an adverse effect on the safety of the communication product, and then monitors and collects data on the operation behavior of the communication product to obtain the behavior monitoring parameters, then uses artificial intelligence technology to perform automatic data processing, and the behavior monitoring coefficient obtained according to the behavior monitoring parameters can comprehensively measure the abnormality of the operation behavior of the communication product, and the larger the behavior monitoring parameter is, the higher the abnormality of the operation behavior is, which has a further adverse effect on the safety of the communication product, and finally an abnormal alarm is issued; The artificial intelligence-based communication product security management system of the present invention adopts artificial intelligence technology, can quickly and accurately identify potential security threats of communication products, improve the efficiency of security detection, and can organize and analyze large amounts of data to improve the accuracy of security detection. It can also promptly issue abnormal alarms for communication products that have abnormal conditions, improve the efficiency and accuracy of security response, realize intelligent security management, meet the security needs of different users, provide users with a more secure and reliable communication environment, ensure communication security and data security, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 It is a principle block diagram of a communication product security management system based on artificial intelligence in the present invention; Figure 2 It is a flow chart of a working method of a communication product security management system based on artificial intelligence in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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.

[0019] Example 1: Please refer to Figure 1 As shown, this embodiment is a communication product safety management system based on artificial intelligence, including the following modules: a status monitoring module, an information processing module, an abnormality identification module, an abnormality warning module and a behavior monitoring module; The state monitoring module is used to obtain the state monitoring parameters of the monitored object and send the state monitoring parameters to the information processing module; wherein the state monitoring parameters include temperature difference information WC, pressure difference information YC, flow difference information LC and operation information YX; The information processing module is used to obtain a state monitoring coefficient ZT according to the state monitoring parameters, and send the state monitoring coefficient ZT to the abnormality discrimination module; and is also used to obtain a behavior monitoring coefficient XW according to the behavior monitoring parameters, and send the behavior monitoring coefficient XW to the abnormality discrimination module; The abnormality identification module is used to generate a state abnormality instruction or a behavior monitoring instruction according to the state monitoring coefficient ZT, and send the state abnormality instruction to the abnormal warning module, and send the behavior monitoring instruction to the behavior monitoring module; it is also used to generate a behavior abnormality instruction according to the behavior monitoring coefficient XW, and send the behavior abnormality instruction to the abnormal warning module; The abnormal warning module is used to sound an abnormal state alarm ring after receiving an abnormal state instruction; and is also used to sound an abnormal behavior alarm ring after receiving an abnormal behavior instruction; Among them, the behavior monitoring module is used to obtain the behavior monitoring parameters of the monitored object after receiving the behavior monitoring instruction, and send the behavior monitoring parameters to the information processing module; wherein, the behavior monitoring parameters include data speed information SS, data deviation information SP and vulnerability information LD.

[0020] Example 2: Please refer to Figure 2 As shown, this embodiment is a working method of a communication product security management system based on artificial intelligence, comprising the following steps: Step 1: The state monitoring module obtains the state monitoring parameters of the monitored object, wherein the state monitoring parameters include temperature difference information WC, pressure difference information YC, flow difference information LC and operation information YX, and sends the state monitoring parameters to the information processing module; Step 2: The information processing module obtains the state monitoring coefficient ZT according to the state monitoring parameters, and sends the state monitoring coefficient ZT to the abnormality discrimination module; Step 3: The abnormality identification module generates a state abnormality instruction or a behavior monitoring instruction according to the state monitoring coefficient ZT, and sends the state abnormality instruction to the abnormality warning module and sends the behavior monitoring instruction to the behavior monitoring module; Step 4: After receiving the abnormal status instruction, the abnormal status alarm module sounds an abnormal status alarm bell; Step 5: After receiving the behavior monitoring instruction, the behavior monitoring module obtains the behavior monitoring parameters of the monitored object, wherein the behavior monitoring parameters include data speed information SS, data deviation information SP and vulnerability information LD, and sends the behavior monitoring parameters to the information processing module; Step 6: The information processing module obtains the behavior monitoring coefficient XW according to the behavior monitoring parameter, and sends the behavior monitoring coefficient XW to the abnormality discrimination module; Step 7: The abnormality identification module generates an abnormal behavior instruction according to the behavior monitoring coefficient XW, and sends the abnormal behavior instruction to the abnormal warning module; Step 8: After receiving the abnormal behavior instruction, the abnormal warning module sounds the abnormal behavior alarm bell.

[0021] Embodiment 3: Based on any of the above embodiments, Embodiment 3 of the present invention is a state monitoring module. The function of the state monitoring module is to obtain state monitoring parameters, wherein the state monitoring parameters include temperature difference information WC, pressure difference information YC, flow difference information LC and operation information YX. The specific process is as follows: The status monitoring module marks the communication products for security management monitoring as monitoring objects; The status monitoring module obtains the real-time temperature of the monitored object and the preset standard temperature, obtains the difference between the two, and marks it as temperature difference information WC; The status monitoring module obtains the real-time voltage of the monitored object, obtains the maximum real-time voltage and the minimum real-time voltage per unit time, obtains the difference between the two, and marks it as the extreme difference value JC, obtains the average value of the real-time voltage per unit time, and marks it as the average voltage value, obtains the difference between the average voltage value and the preset standard voltage, and marks it as the rated difference value EC, obtains the extreme difference value JC and the rated difference value EC for quantification, extracts the values ​​of the extreme difference value JC and the rated difference value EC, and substitutes them into the formula for calculation. According to the formula The pressure difference information YC is obtained; wherein c1 and c2 are preset proportional coefficients corresponding to the set extreme difference value JC and rated difference value EC, respectively, c1 and c2 satisfy c1+c2=1, 0<c2<c1<1, and c1=0.63 and c2=0.37; The state monitoring module obtains the real-time current of the monitored object, obtains the average value of the real-time current in a unit time, and marks it as the average current value, obtains the difference between the average current value and the preset standard current, and marks it as the current difference information LC; The status monitoring module obtains the total running time and the total number of startups of the monitored object from the first use to the current time, and marks them as the running time value YS and the startup number value QC respectively, draws a diamond figure with the values ​​of the running time value YS and the startup number value QC as the diagonal lines of the diamond, obtains the area of ​​the diamond figure, and marks it as the running information YX; The status monitoring module sends the temperature difference information WC, the pressure difference information YC, the flow difference information LC and the operation information YX to the information processing module.

[0022] Embodiment 4: Based on any of the above embodiments, Embodiment 4 of the present invention is an information processing module, which has two functions; One of the functions is to obtain the state monitoring coefficient ZT. The specific process is as follows: The information processing module normalizes the temperature difference information WC, the pressure difference information YC, the flow difference information LC and the operation information YX, and quantifies the values ​​of the temperature difference information WC, the pressure difference information YC, the flow difference information LC and the operation information YX according to the preset state monitoring model to obtain the state monitoring coefficient ZT; The condition monitoring model is as follows: Where: κ is the preset error adjustment factor, taking κ=0.927; Both π and e are mathematical constants; z1, z2, z3 and z4 are preset weight factors corresponding to the set temperature difference information WC, pressure difference information YC, flow difference information LC and operation information YX respectively. z1, z2, z3 and z4 satisfy z1>z3>z2>z4>1.615, and z1=3.51, z2=2.38, z3=2.79, z4=1.93; The information processing module sends the state monitoring coefficient ZT to the abnormality discrimination module; The second function is to obtain the behavior monitoring coefficient XW. The specific process is as follows: The information processing module normalizes the data rate information SS, the data deviation information SP and the vulnerability information LD, and quantifies the values ​​of the data rate information SS, the data deviation information SP and the vulnerability information LD according to the preset behavior monitoring model to obtain the behavior monitoring coefficient XW; Among them, the behavior monitoring model is as follows: Where: λ is the preset error adjustment factor, and λ=1.125 is taken; Both π and e are mathematical constants; w1, w2 and w3 are the preset weight factors corresponding to the set data rate information SS, data bias information SP and vulnerability information LD respectively. w1, w2 and w3 satisfy w2>w3>w1>2.117, and w1=2.56, w2=4.11, w3=3.49; The information processing module sends the behavior monitoring coefficient XW to the abnormality discrimination module.

[0023] Embodiment 5: Based on any of the above embodiments, Embodiment 5 of the present invention is an abnormality discrimination module, and the abnormality discrimination module has two functions; One of them is to generate status abnormality instructions or behavior monitoring instructions. The specific process is as follows: The abnormality discrimination module compares the state monitoring coefficient ZT with the preset state monitoring threshold ZTy. The comparison results are as follows: If the state monitoring coefficient ZT ≥ the state monitoring threshold ZTy, a state abnormality instruction is generated and sent to the abnormal warning module; If the state monitoring coefficient ZT < the state monitoring threshold ZTy, a behavior monitoring instruction is generated and sent to the behavior monitoring module; The second function is to generate abnormal behavior instructions. The specific process is as follows: The abnormality discrimination module compares the behavior monitoring coefficient XW with the preset behavior monitoring threshold XWy. The comparison results are as follows: If the behavior monitoring coefficient XW≥behavior monitoring threshold XWy, a behavior abnormality instruction is generated and sent to the abnormal warning module.

[0024] Embodiment 6: Based on any of the above embodiments, Embodiment 6 of the present invention is an abnormal warning module, and the abnormal warning module has two functions; One function is to sound an abnormal status alarm bell after receiving an abnormal status command; The second function is to sound an abnormal behavior alarm bell after receiving an abnormal behavior instruction.

[0025] Embodiment 7: Based on any of the above embodiments, Embodiment 7 of the present invention is a behavior monitoring module. The function of the behavior monitoring module is to obtain behavior monitoring parameters, wherein the behavior monitoring parameters include data speed information SS, data deviation information SP and vulnerability information LD. The specific process is as follows: After receiving the behavior monitoring instruction, the behavior monitoring module obtains the data transmission rate of the monitored object per unit time and the preset standard rate, obtains the difference between the two, and marks it as data rate information SS; The behavior monitoring module obtains all the data sent by the monitoring object within a unit time, and marks it as the data sent by the communication product, obtains the total number of bytes of the data sent by the communication product and the total number of bytes of the data received by the data receiving object of the communication product, obtains the difference between the two, and marks it as the data transmission value SF, obtains all the received data per unit time of the monitoring object, and marks it as the data received by the communication product, obtains the total number of bytes of the data received by the communication product and the total number of bytes of the data sent by the data sending object of the communication product receiving data, obtains the difference between the two, and marks it as the data connection value SJ, quantifies the data transmission value SF and the data connection value SJ, extracts the values ​​of the data transmission value SF and the data connection value SJ, and substitutes them into the formula for calculation. According to the formula Obtain the digital deviation information SP; wherein e is a mathematical constant, s1 and s2 are respectively the preset proportional coefficients corresponding to the set digital transmission value SF and the digital connection value SJ, s1 and s2 satisfy s1+s2=1, 0<s2<s1<1, and s1=0.59, s2=0.41; The behavior monitoring module obtains the number of vulnerabilities that appear in the monitored object within the preset time before the current moment, and marks it as the newly added value XZ, obtains the total number of unrepaired vulnerabilities in the monitored object at the current moment, and marks it as the existing value XC, quantifies the newly added value XZ and the existing value XC, extracts the values ​​of the newly added value XZ and the existing value XC, and substitutes them into the formula for calculation. Get vulnerability information LD; where e and π are mathematical constants, x1 and x2 are preset proportional coefficients corresponding to the set new value XZ and the existing value XC, respectively, x1 and x2 satisfy x1+x2=1, 0<x1<x2<1, and x1=0.38 and x2=0.62; The behavior monitoring module sends the data speed information SS, data deviation information SP and vulnerability information LD to the information processing module.

[0026] Based on the above embodiments 1-7, the working principle of the present invention is as follows: The present invention is an artificial intelligence-based communication product safety management system, which obtains the state monitoring parameters of the monitored object through a state monitoring module, obtains the state monitoring coefficient according to the state monitoring parameters through an information processing module, generates a state abnormality instruction or a behavior monitoring instruction according to the state monitoring coefficient through an abnormality discrimination module, sounds a state abnormality alarm ring after receiving the state abnormality instruction through an abnormal warning module, obtains the behavior monitoring parameters of the monitored object after receiving the behavior monitoring instruction through a behavior monitoring module, obtains the behavior monitoring coefficient according to the behavior monitoring parameters through an information processing module, generates a behavior abnormality instruction according to the behavior monitoring coefficient through an abnormality discrimination module, and sounds a behavior abnormality alarm ring after receiving the behavior abnormality instruction through an abnormal warning module; the system first monitors and collects data on the operating status of the communication product to obtain the state monitoring parameters, and then uses artificial intelligence technology to perform automatic data processing. The state monitoring coefficient obtained according to the state monitoring parameters can comprehensively measure the degree of abnormality of the operating status of the communication product, and the state monitoring coefficient The larger the value, the higher the abnormality of the operating status, which will have an adverse impact on the security of the communication product. The operating behavior of the communication product is then monitored and data is collected to obtain behavior monitoring parameters. Artificial intelligence technology is then used to automate data processing. The behavior monitoring coefficient obtained based on the behavior monitoring parameters can comprehensively measure the degree of abnormality of the operating behavior of the communication product. The larger the behavior monitoring parameter, the higher the degree of abnormality of the operating behavior, which will further have an adverse impact on the security of the communication product. Finally, an abnormal alarm is issued. The system uses artificial intelligence technology to quickly and accurately identify potential security threats to communication products, improve the efficiency of security detection, and can organize and analyze large amounts of data to improve the accuracy of security detection. It can also promptly issue abnormal alarms for communication products that have abnormal conditions, improve the efficiency and accuracy of security response, and realize intelligent security management. It can meet the security needs of different users, provide users with a safer and more reliable communication environment, ensure communication security and data security, and has broad application prospects.

[0027] It should be further explained that the above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technical personnel in this field according to actual conditions.

[0028] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0029] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A communication product security management system based on artificial intelligence, characterized in that: include: A behavior monitoring module, used to obtain behavior monitoring parameters of the monitored object and send the behavior monitoring parameters to the information processing module; wherein the behavior monitoring parameters include data speed information SS, data deviation information SP and vulnerability information LD; An information processing module, used for obtaining a behavior monitoring coefficient XW according to the behavior monitoring parameter, and sending the behavior monitoring coefficient XW to the abnormality discrimination module; The specific process of the information processing module obtaining the behavior monitoring coefficient XW is as follows: The values ​​of the speed information SS, the deviation information SP and the vulnerability information LD are quantified according to the preset behavior monitoring model to obtain the behavior monitoring coefficient XW; Among them, the behavior monitoring model is as follows: Where: λ is the preset error adjustment factor; Both π and e are mathematical constants; w1, w2 and w3 are preset weight factors corresponding to the set data rate information SS, data bias information SP and vulnerability information LD respectively; An abnormality identification module is used to generate an abnormal behavior instruction according to the behavior monitoring coefficient XW, and send the abnormal behavior instruction to the abnormal warning module; The abnormal warning module is used to sound an abnormal behavior alarm ring after receiving an abnormal behavior instruction.

2. According to claim 1, a communication product security management system based on artificial intelligence is characterized in that: The specific process of the behavior monitoring module obtaining the behavior monitoring parameters is as follows: After receiving the behavior monitoring instruction, the data transmission rate of the monitored object per unit time and the preset standard rate are obtained, the difference between the two is obtained, and it is marked as data rate information SS; Obtain all the data sent by the monitoring object per unit time, and mark it as the data sent by the communication product, obtain the total number of bytes of the data sent by the communication product and the total number of bytes of the data received by the data receiving object of the communication product, obtain the difference between the two, and mark it as the data transmission value SF, obtain all the received data per unit time of the monitoring object, and mark it as the data received by the communication product, obtain the total number of bytes of the data received by the communication product and the total number of bytes of the data sent by the data sending object of the communication product receiving data, obtain the difference between the two, and mark it as the data connection value SJ, quantify the data transmission value SF and the data connection value SJ, and use the formula Obtain digital deviation information SP; wherein e is a mathematical constant, s1 and s2 are preset proportional coefficients corresponding to the set digital transmission value SF and the digital connection value SJ respectively; Get the number of vulnerabilities that appeared in the preset time before the current moment of the monitored object, and mark it as the newly added value XZ. Get the total number of unrepaired vulnerabilities in the monitored object at the current moment, and mark it as the existing value XC. Quantify the newly added value XZ and the existing value XC according to the formula Get the vulnerability information LD; where e and π are mathematical constants, x1 and x2 are the preset proportional coefficients corresponding to the set new value XZ and the existing value XC respectively; The data speed information SS, data deviation information SP and vulnerability information LD are sent to the information processing module.

3. According to claim 1, a communication product security management system based on artificial intelligence is characterized in that: The specific process of the abnormal identification module generating abnormal behavior instructions is as follows: The behavior monitoring coefficient XW is compared with the preset behavior monitoring threshold XWy, and the comparison results are as follows: If the behavior monitoring coefficient XW≥behavior monitoring threshold XWy, a behavior abnormality instruction is generated and sent to the abnormal warning module.

4. The communication product security management system based on artificial intelligence according to claim 1 is characterized in that: The state monitoring module is used to obtain the state monitoring parameters of the monitored object and send the state monitoring parameters to the information processing module; wherein the state monitoring parameters include temperature difference information WC, pressure difference information YC, flow difference information LC and operation information YX.

5. According to claim 4, the communication product security management system based on artificial intelligence is characterized in that: The specific process of the state monitoring module obtaining the state monitoring parameters is as follows: Mark the communication products that are subject to security management monitoring as monitoring objects; Obtain the real-time temperature of the monitored object and the preset standard temperature, obtain the difference between the two, and mark it as temperature difference information WC; Get the real-time voltage of the monitored object, get the maximum real-time voltage and the minimum real-time voltage per unit time, get the difference between the two, and mark it as the extreme difference value JC, get the average value of the real-time voltage per unit time, and mark it as the average voltage value, get the difference between the average voltage value and the preset standard voltage, and mark it as the rated difference value EC, get the extreme difference value JC and the rated difference value EC for quantification, and according to the formula The pressure difference information YC is obtained; wherein c1 and c2 are preset proportional coefficients corresponding to the set extreme difference value JC and rated difference value EC respectively; Obtain the real-time current of the monitored object, obtain the average value of the real-time current in a unit time, and mark it as the average current value, obtain the difference between the average current value and the preset standard current, and mark it as the current difference information LC; Obtain the total running time and total number of startups of the monitored object from the first use to the current time, and mark them as running time value YS and startup number value QC respectively, draw a diamond figure with the values ​​of running time value YS and startup number value QC as the diagonal lines of the diamond, obtain the area of ​​the diamond figure, and mark it as running information YX; The temperature difference information WC, the pressure difference information YC, the flow difference information LC and the operation information YX are sent to the information processing module.

6. The communication product security management system based on artificial intelligence according to claim 1 is characterized in that: The information processing module is also used to obtain a state monitoring coefficient ZT according to the state monitoring parameters, and send the state monitoring coefficient ZT to the abnormality determination module.

7. The artificial intelligence-based communication product security management system according to claim 6, characterized in that: The specific process of the information processing module obtaining the state monitoring coefficient ZT is as follows: The values ​​of the temperature difference information WC, the pressure difference information YC, the flow difference information LC and the operation information YX are quantified and calculated according to the preset state monitoring model to obtain the state monitoring coefficient ZT; The condition monitoring model is as follows: Where: κ is the preset error adjustment factor; Both π and e are mathematical constants; z1, z2, z3 and z4 are preset weight factors corresponding to the set temperature difference information WC, pressure difference information YC, flow difference information LC and operation information YX respectively; The state monitoring coefficient ZT is sent to the abnormality discrimination module.

8. The communication product security management system based on artificial intelligence according to claim 1 is characterized in that: The abnormality identification module is also used to generate a state abnormality instruction or a behavior monitoring instruction according to the state monitoring coefficient ZT, and send the state abnormality instruction to the abnormal warning module and send the behavior monitoring instruction to the behavior monitoring module.

9. The artificial intelligence-based communication product security management system according to claim 8, characterized in that: The specific process of the abnormality identification module generating a state abnormality instruction or a behavior monitoring instruction is as follows: The condition monitoring coefficient ZT is compared with the preset condition monitoring threshold ZTy, and the comparison results are as follows: If the state monitoring coefficient ZT ≥ the state monitoring threshold ZTy, a state abnormality instruction is generated and sent to the abnormal warning module; If the state monitoring coefficient ZT<the state monitoring threshold ZTy, a behavior monitoring instruction is generated and sent to the behavior monitoring module.

10. The communication product security management system based on artificial intelligence according to claim 1, characterized in that: The abnormality warning module is also used to sound an abnormality alarm ring after receiving an abnormality instruction.

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