CN118827000A - Intelligent mining loading and transportation monitoring method and system - Google Patents

Abstract

The present invention belongs to the field of safety monitoring technology, and specifically relates to a method and system for monitoring mine loading and transportation based on intelligence, wherein the method comprises: using the encoding result of key monitoring data as a plaintext binary sequence, selecting a target combination mode according to the size of a second chaotic value in a second chaotic sequence, obtaining a combination character of the previous ciphertext data in the target combination mode, obtaining a target child node corresponding to the combination character on a Huffman tree, using another child node of the parent node of the target child node as a starting node, and walking from the starting node on the Huffman tree according to the order and value of the plaintext binary data in the plaintext binary sequence until obtaining a termination node with a preset character, and using the preset character corresponding to the termination node as ciphertext data. The present invention improves the security of key monitoring data, thereby improving the security of the loading and transportation process.

Description

Intelligent mining loading and transportation monitoring method and system

Technical Field

The present invention relates to the field of safety monitoring technology, and more specifically, to an intelligent mine loading and transportation monitoring method and system.

Background Art

Intelligent coal mines are the core technical support for the high-quality development of the coal industry. They deeply integrate artificial intelligence, industrial Internet of Things, cloud computing, big data, robots, intelligent equipment, etc. with modern coal development and utilization to form an intelligent system with comprehensive perception, real-time interconnection, analysis and decision-making, autonomous learning, dynamic prediction, and collaborative control. They can realize the intelligent operation of coal mine development, mining, transportation, ventilation, washing, safety assurance, and operation and management, which is of great significance to improving the safety production level of coal mines and ensuring a stable supply of coal.

Among them, during the loading and transportation process of mines, through key monitoring data such as load and loading volume data, loading and unloading time data, vehicle location data, and operating status data, comprehensive monitoring and intelligent management of the loading and transportation process of mines can be achieved, thereby improving loading and transportation efficiency, reducing costs, and ensuring the safety of the loading and transportation process; however, there is a risk of leakage of key monitoring data during transmission, which affects the safety of the loading and transportation process.

In the related technology, for example, the Chinese patent application document with application publication number CN115314259B discloses a safety control method, client, server and system for a mine transportation system, which increases the security of communication data transmission through two corresponding encryption and decryption on both the client and server sides.

However, existing encryption methods usually encrypt plaintext of a fixed length into ciphertext of a fixed length, which has a strong regularity. This causes attackers to conduct statistical analysis attacks on existing encryption methods based on the regularity between plaintext and ciphertext.

In order to ensure the security of key monitoring data, an encryption algorithm that can resist statistical analysis attacks is urgently needed to encrypt key monitoring data.

Summary of the invention

In order to solve the above-mentioned technical problem of urgently needing an encryption algorithm that can resist statistical analysis attacks to achieve encryption of key monitoring data, the present invention provides solutions in the following multiple aspects.

The present invention provides a mine loading and transportation monitoring method based on intelligence, comprising: using the encoding result of key monitoring data as a plaintext binary sequence; generating two chaotic sequences, namely a first chaotic sequence and a second chaotic sequence, according to a key and a chaotic mapping function; constructing a Huffman tree according to the first chaotic sequence and preset characters; encrypting the plaintext binary sequence according to the second chaotic sequence and the Huffman tree to obtain a ciphertext sequence composed of a plurality of ciphertext data, wherein the encryption step comprises: obtaining a plurality of combination modes of all preset characters, wherein the combination mode comprises a plurality of combinations, each combination comprises any two preset characters, and the two preset characters in the combination are combined characters of each other. character, and the preset characters between multiple combinations are not repeated; according to the size of the second chaotic value in the second chaotic sequence, a target combination method is selected from all the combination methods; in the target combination method, a combination character of the previous ciphertext data is obtained; a target child node corresponding to the combination character on the Huffman tree is obtained; another child node of the parent node of the target child node is used as a starting node; according to the order and value of the plaintext binary data in the plaintext binary sequence, a walk is started from the starting node on the Huffman tree until a termination node with a preset character is obtained; the preset character corresponding to the termination node is used as the ciphertext data; the plaintext binary data contained in the walk path is deleted from the plaintext binary sequence.

When obtaining each ciphertext character, the number of plaintext binary data used is not fixed. Compared with the existing encryption method that encrypts a plaintext of a fixed length into a ciphertext of a fixed length, the encryption method of the present invention obtains a ciphertext sequence with poor regularity, so that the encryption method of the present invention can resist statistical analysis attacks, improve the security of key monitoring data, and further improve the security of the loading and transportation process;

Furthermore, in the encryption method of the present invention, the starting node obtained depends on the target combination mode obtained in each encryption and the ciphertext data obtained in the previous encryption. Therefore, even if the plaintext binary data is the same, the ciphertext data finally obtained will be different due to the different starting nodes. Therefore, the encryption method of the present invention destroys the association between the plaintext binary data and the ciphertext data, increases the difficulty of brute force cracking, improves the security of key monitoring data, and further improves the security of the loading and transportation process;

Furthermore, the present invention obtains the starting node for each encryption based on the ciphertext data obtained in the previous encryption. Therefore, when decoding the encryption result of the key monitoring data, that is, the ciphertext sequence, any slight change caused by a decryption error of the ciphertext data will cause a change in the indistinguishability of the decryption result of the ciphertext sequence. Therefore, the ciphertext sequence obtained according to the encryption method of the present invention has a strong avalanche effect, which improves the security of the key monitoring data and further improves the security of the loading and transportation process.

Preferably, the key is generated according to parameters and initial conditions of the chaotic mapping system.

Based on the pseudo-randomness, sensitivity to initial conditions, non-periodicity and long-term unpredictability of the chaotic mapping function, the present invention generates a key according to the parameters and initial conditions of the chaotic mapping system, so that the encryption algorithm of the present invention has higher security.

Preferably, the method of constructing a Huffman tree based on the first chaotic sequence and preset characters includes: taking the cumulative sum of the first R first chaotic values in the first chaotic sequence as the first numerical value, where R represents the number of preset characters; taking the ratio of the i-th first chaotic value in the first chaotic sequence to the first numerical value as the preset frequency of the i-th preset character, where i takes all integers in the range of [1,R]; and constructing a Huffman tree based on the preset frequencies of all preset characters.

Preferably, the method of selecting a target combination from all combinations based on the size of the second chaotic value in the second chaotic sequence includes: recording the number of combinations of all preset characters as a first number; rounding the product of the second chaotic value in the second chaotic sequence and the first number as a second numerical value S; and taking the Sth combination among all combinations as the target combination.

According to the size of the second chaotic value in the second chaotic sequence, a target combination mode is selected from all the combination modes, so that the starting node obtained in each encryption is different, thereby destroying the correlation between the plaintext binary data and the ciphertext data.

Preferably, the encryption step further comprises: repeating the encryption step until the plaintext binary sequence is empty or a termination node with a preset character cannot be obtained, and then stopping the iteration.

Preferably, the method further comprises: during the first encryption, taking the root node on the Huffman tree as the starting node.

The integrity and feasibility of the encryption steps of the encryption method of the present invention are guaranteed.

Preferably, the method further comprises: when the obtained starting node is a node having a preset character, using the preset character corresponding to the starting node as ciphertext data.

Preferably, the method further comprises: storing the ciphertext sequence on a server; storing the preset characters and the key on an offline device, which can only be used by persons with access authority.

The key is used for decryption. Storing the key on an offline device can improve security because the offline device is not vulnerable to network attacks or remote intrusions.

In the second aspect, the present invention provides an intelligent mine loading and transportation monitoring system, which adopts the following technical solutions:

The intelligent mining loading and transportation monitoring system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent mining loading and transportation monitoring method is implemented.

By adopting the above technical solution, the above intelligent mine loading and transportation monitoring method is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is made according to the memory and the processor for easy use.

The beneficial effects of the present invention are:

The encryption method of the present invention has poor regularity in the ciphertext sequence, which enables the encryption method of the present invention to resist statistical analysis attacks, improve the security of key monitoring data, and further improve the security of the loading and transportation process;

Furthermore, the encryption method of the present invention destroys the association between plaintext binary data and ciphertext data, increases the difficulty of brute force cracking, improves the security of key monitoring data, and further improves the security of the loading and transportation process;

Furthermore, the ciphertext sequence obtained by the encryption method according to the present invention has a strong avalanche effect, which improves the security of key monitoring data and further improves the security of the loading and transportation process.

Furthermore, the present invention generates a key according to the parameters and initial conditions of the chaotic mapping system, and stores the key on an offline device, so that the encryption algorithm of the present invention has higher security.

BRIEF DESCRIPTION OF THE DRAWINGS

By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

FIG1 is a flow chart schematically showing an intelligent mine loading and transportation monitoring method according to the present invention;

FIG2 schematically shows a Huffman tree constructed according to the first chaotic sequence and all preset characters in the present invention;

FIG3 schematically shows a starting node for the first encryption, a starting node for the second encryption, and a starting node for the third encryption.

DETAILED DESCRIPTION

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 described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

The embodiment of the present invention discloses an intelligent mine loading and transportation monitoring method, referring to FIG. 1 , comprising steps S1 to S4:

S1. The encoding result of the key monitoring data is used as a plaintext binary sequence.

It should be noted that there is a risk of leakage of key monitoring data during transmission, which affects the safety of the loading and transportation process. Therefore, the key monitoring data needs to be encrypted. The present invention uses the encoding result of the key monitoring data as a plaintext binary sequence.

Specifically, during the mine loading and transportation process, through key monitoring data such as vehicle location data, operating status data, load and load data, loading and unloading time data, fuel consumption data, vehicle health status data, loading process monitoring data, and environmental key monitoring data, comprehensive management and intelligent control of the mine loading and transportation process can be achieved, thereby improving transportation efficiency, reducing costs, and ensuring the safety of the transportation process.

Among them, the vehicle location data includes the real-time location of the vehicle, and the running status data includes the real-time speed, driving direction and whether the vehicle is driving on the prescribed route. By monitoring the vehicle location data and running status data, the safety and efficiency of transportation can be ensured;

By monitoring the vehicle's load and loading data, ensure that the vehicle is not overloaded and load it reasonably according to the actual situation to improve transportation efficiency and safety;

By monitoring the loading and unloading time data of vehicles, the transportation plan can be optimized and the work process can be improved to improve the loading and unloading efficiency and transportation efficiency;

By monitoring the vehicle's fuel consumption data, fuel cost control and energy conservation and emission reduction can be achieved;

The vehicle health status data includes the vehicle's mechanical status, engine operation, lubricating oil temperature, water temperature and other parameters. By monitoring the vehicle's health status data, vehicle failures can be discovered and handled in a timely manner to ensure the smooth progress of the transportation process;

The loading process monitoring data includes loading speed, loading uniformity, use of loading equipment, etc. The loading process monitoring data is monitored to ensure the safety and efficiency of the loading process;

Key environmental monitoring data include the environmental conditions around the transportation route and the surrounding terrain and road conditions, such as climate, temperature, humidity, wind and other factors. By monitoring key environmental monitoring data, the safety and adaptability of the transportation process can be ensured.

Furthermore, the key monitoring data is encoded by a conventional encoding method, and the encoding result of the key monitoring data is used as a plaintext binary sequence; the plaintext binary sequence is composed of a plurality of plaintext binary data.

The conventional encoding methods include ASCII encoding, Unicode encoding, UTF-8 encoding, etc.

S2. Generate two chaotic sequences according to the key and the chaotic mapping function.

It should be noted that chaotic mapping functions are pseudo-random, sensitive to initial conditions, non-periodic and long-term unpredictable, and are often used as key generators.

Specifically, two keys are generated according to the parameters and initial conditions of the chaotic mapping system; the function of the chaotic mapping system is iterated multiple times according to one of the keys, and a first chaotic value with a value range of [0,1] is obtained in each iteration, and a sequence composed of all the first chaotic values is used as a first chaotic sequence; the function of the chaotic mapping system is iterated multiple times according to the other key, and a second chaotic value with a value range of [0,1] is obtained in each iteration, and a sequence composed of all the second chaotic values is used as a second chaotic sequence.

The chaotic mapping system includes but is not limited to a Logistic chaotic mapping system, a Sine chaotic mapping system, a Singer chaotic mapping system, a Circle chaotic mapping system, and a Henon chaotic mapping system.

S3. Construct a Huffman tree according to the first chaotic sequence and the preset characters.

Specifically, R characters are selected from all characters without duplication as preset characters, and the characters include but are not limited to Chinese characters, English letters, and numbers.

Wherein, R represents the number of preset characters, and R is equal to a preset value, which is an even number in the range of [10,200]. In this embodiment, the preset value is 40.

Further, the cumulative sum of the first R first chaotic values in the first chaotic sequence is used as the first value; the ratio of the i-th first chaotic value in the first chaotic sequence to the first value is used as the preset frequency of the i-th preset character; then the preset frequency of the i-th preset character satisfies the expression:

In the formula, represents the preset frequency of the i-th preset character, i takes all integers in the range [1, R], , denote the i-th first chaotic value and the j-th first chaotic value in the first chaotic sequence, R denotes the number of preset characters, Indicates the first value.

Furthermore, a Huffman tree is constructed according to the preset frequencies of all preset characters; in the constructed Huffman tree, only some nodes have the preset characters, and other nodes do not have the preset characters.

Exemplarily, the preset characters include: a, b, c, d, m, n, x, y, and the preset frequencies of the preset characters are: 0.118, 0.088, 0.235, 0.029, 0.265, 0.118, 0.029, 0.118, respectively. According to the preset frequencies of all preset characters, the Huffman tree constructed is shown in Figure 2.

S4. According to the second chaotic sequence and the Huffman tree, the plaintext binary sequence is encrypted to obtain a ciphertext sequence composed of multiple ciphertext data.

It should be noted that the existing encryption method usually encrypts plaintext of a fixed length into ciphertext of a fixed length, which has a strong regularity, resulting in attackers being able to carry out statistical analysis attacks on the existing encryption method based on the regularity between the plaintext and the ciphertext; in order to ensure the security of key monitoring data, the present invention proposes an encryption algorithm that can resist statistical analysis attacks to achieve encryption of key monitoring data.

Specifically, according to the second chaotic sequence and the Huffman tree, the plaintext binary sequence is encrypted to obtain a ciphertext sequence composed of a plurality of ciphertext data, and the encryption step includes:

1. Obtain multiple combinations of all preset characters, wherein the combination includes multiple combinations, each combination includes any two preset characters, the two preset characters in the combination are combined characters of each other, and the preset characters between multiple combinations are not repeated.

Exemplarily, when the characters "x", "y", "m", and "n" are used as preset data, there are three combinations, namely {(x,y), (m,n)}, {(x,m), (n,y)}, and {(x,n), (m,y)}; wherein, in the first combination {(x,y), (m,n)}, (x,y) is a combination, in which the characters "x" and "y" are combined characters, and {(m,n)} is a combination, in which the characters "m" and "n" are combined characters; In the second combination {(x,m),(n,y)}, (x,m) is a combination in which the characters "x" and "m" are combined characters, and (n,y)} is a combination in which the characters "n" and "y" are combined characters. In the third combination {(x,n),(m,y)}, (x,n) is a combination in which the characters "x" and "n" are combined characters, and (m,y)} is a combination in which the characters "m" and "y" are combined characters.

2. According to the size of the second chaotic value in the second chaotic sequence, a target combination method is selected from all combinations, including: recording the number of combinations of all preset characters as a first number; rounding the product of the second chaotic value in the second chaotic sequence and the first number as a second numerical value S; and selecting the Sth combination method among all combinations as the target combination method.

3. In the target combination mode, obtain the combination character of the previous ciphertext data.

Exemplarily, when the target combination method is {(a,n),(b,c),(x,m),(d,y)} and the previous ciphertext data is "b", in the target combination method, the combination character of the preset character "b" is the preset character "c", and therefore, the combination character of the previous ciphertext data is the preset character "c".

It should be noted that the present invention obtains the starting node for each encryption based on the ciphertext data obtained in the previous encryption. Therefore, when decoding the encryption result of the key monitoring data, that is, the ciphertext sequence, any slight change caused by a decryption error of the ciphertext data will cause a change in the indistinguishability of the decryption result of the ciphertext sequence. Therefore, the ciphertext sequence obtained according to the encryption method of the present invention has a strong avalanche effect, which improves the security of the key monitoring data and further improves the safety of the loading and transportation process.

4. Obtain the target child node corresponding to the combined character in the Huffman tree; and use another child node of the parent node of the target child node as the starting node.

It should be noted that during the first encryption, the root node on the Huffman tree is used as the starting node.

It should be noted that in the encryption method of the present invention, the starting node obtained depends on the target combination method obtained each time the encryption is performed and the ciphertext data obtained in the previous encryption. Therefore, even if the plaintext binary data is the same, the ciphertext data finally obtained will be different due to the different starting nodes. Therefore, the encryption method of the present invention destroys the association between the plaintext binary data and the ciphertext data, increases the difficulty of brute force cracking, improves the security of key monitoring data, and thereby improves the security of the loading and transportation process.

5. According to the order and value of the plaintext binary data in the plaintext binary sequence, walk from the starting node on the Huffman tree until a termination node with a preset character is obtained; the preset character corresponding to the termination node is used as the ciphertext data.

It should be noted that when the starting node is a node with a preset character, since there is no subtree under the starting node at this time, the ending node with the preset character obtained by walking from the starting node is the starting node itself, and the walking path length is 0. At this time, the preset character corresponding to the starting node is used as the ciphertext data obtained by this encryption;

6. Delete the plaintext binary data contained in the wandering path from the plaintext binary sequence.

7. Repeat the encryption steps until the plaintext binary sequence is empty or the termination node with the preset character cannot be obtained, and then stop the iteration.

It should be noted that when obtaining each ciphertext character, the corresponding number of plaintext binary data used in the present invention is not fixed. Compared with the existing encryption method that encrypts a plaintext of a fixed length into a ciphertext of a fixed length, the encryption method of the present invention obtains a ciphertext sequence with poor regularity, which enables the encryption method of the present invention to resist statistical analysis attacks, improves the security of key monitoring data, and further improves the security of the loading and transportation process.

Exemplarily, when the plaintext binary sequence is {1,0,1,1,0,1,0,1,1,1,0,0,0,1,0,…}, the plaintext binary sequence is encrypted according to the second chaotic sequence and the Huffman tree shown in FIG2 , and the specific process is as follows:

(1) When performing the first encryption, the root node on the Huffman tree is used as the starting node. For details, please refer to the “Starting Node for the First Encryption” shown in FIG. 3 . On the Huffman tree, according to the plaintext binary data in the plaintext binary sequence, walk from the starting node until a termination node with a preset character is obtained. At this time, the preset character corresponding to the termination node obtained is “b”. At this time, the walking path is the path “1011” composed of the first four plaintext binary data in the plaintext binary sequence. The preset character “b” corresponding to the termination node is used as the first ciphertext data. The plaintext binary data contained in the walking path “1011” is deleted from the plaintext binary sequence, that is, the first four plaintext binary data in the plaintext binary sequence are deleted, and the new plaintext binary sequence obtained is {0,1,0,1,1,1,0,0,0,1,0,…}.

(2) When performing the second encryption, according to the size of the second chaotic value in the second chaotic sequence, the target combination selected from all the combination methods is {(a,n),(b,c),(x,m),(d,y)}, the previous ciphertext data is the preset character "b", and in the target combination method, the combination character of the preset character "b" is the preset character "c", so the combination character of the previous ciphertext data is the preset character "c"; obtain the target child node corresponding to the combination character on the Huffman tree, and use another child node of the parent node of the target child node as the starting node. For details, please refer to the "Starting Node for Second Encryption" shown in Figure 3; in the Huffman tree According to the plaintext binary data in the new plaintext binary sequence, walk from the starting node until the termination node with the preset character is obtained. The preset character corresponding to the termination node is "a". The walking path is the path "0" composed of the first plaintext binary data in the new plaintext binary sequence. The preset character "a" corresponding to the termination node is used as the second ciphertext data. The plaintext binary data contained in the walking path "0" is deleted from the new plaintext binary sequence, that is, the first plaintext binary data in the new plaintext binary sequence is deleted, and the new plaintext binary sequence is {1,0,1,1,1,0,0,0,1,0,…}.

(3) When performing the third encryption, according to the size of the second chaotic value in the second chaotic sequence, the target combination selected from all the combination methods is {(a,m),(d,c),(x,b),(n,y)}, the previous ciphertext data is the preset character "a", and in the target combination method, the combination character of the preset character "a" is the preset character "m", so the combination character of the previous ciphertext data is the preset character "m"; obtain the target child node corresponding to the combination character on the Huffman tree, and use another child node of the parent node of the target child node as the starting node. For details, please refer to the "Starting Node for the Third Encryption" shown in Figure 3; on the Huffman tree, According to the plaintext binary data in the new plaintext binary sequence, walk from the starting node until the termination node with the preset character is obtained. The preset character corresponding to the termination node is "x". The walking path is the path "101" composed of the first three plaintext binary data in the new plaintext binary sequence. The preset character "x" corresponding to the termination node is used as the third ciphertext data. The plaintext binary data contained in the walking path "101" is deleted from the new plaintext binary sequence, that is, the first three plaintext binary data in the new plaintext binary sequence are deleted, and the new plaintext binary sequence is {1,0,1,1,1,0,0,0,1,0,…}.

(4) The iteration is terminated in this way until the plaintext binary sequence is empty or the termination node with the preset character cannot be obtained.

Furthermore, the ciphertext sequence is the encryption result of the crew data, and the ciphertext sequence is stored, and the ciphertext sequence is stored on the server; the obtained preset characters and keys are stored on an offline device, such as a USB flash drive, a disk, a solid-state drive, etc. In order to ensure the security of the crew data, only personnel with access authority can use the offline device storing the preset characters and keys, and the crew data is obtained by connecting the offline device storing the preset characters and keys with the server storing the ciphertext sequence, and decrypting the ciphertext sequence using the preset characters and keys.

The decryption process is as follows: two chaotic sequences are generated according to a key and a chaotic mapping function; a Huffman tree is constructed according to the first chaotic sequence and all preset characters; in the process of decrypting each ciphertext data in the ciphertext sequence according to the second chaotic sequence and the Huffman tree, a combination method of all preset characters when decrypting the ciphertext data is determined according to the second chaotic sequence; in the combination method, a combination character of the previous ciphertext data is obtained; another child node of the parent node of the node where the combination character is located is used as the starting node; on the Huffman tree, a path is obtained starting from the starting node until the node corresponding to the ciphertext data is reached, and the binary data corresponding to the obtained path is added to the plaintext binary sequence; the next ciphertext data is decrypted until the decryption results of all ciphertext data are obtained, at which time, the plaintext binary sequence is decoded by encoding, and the decoding result obtained is the passenger data.

It should be noted that when decrypting the first ciphertext data, the root node on the Huffman tree is used as the starting node.

It should be noted that the present invention generates a key according to the parameters and initial conditions of the chaotic mapping system, and stores the key on an offline device, so that the encryption algorithm of the present invention has higher security.

The embodiment of the present invention also discloses an intelligent mine loading and transportation monitoring system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent mine loading and transportation monitoring method according to the present invention is implemented.

The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, alternatives to the embodiments of the present invention described herein may be employed.

Claims (9)

1. A method for monitoring mine loading and transportation based on intelligence, characterized by comprising: The encoding result of the key monitoring data is used as a plain text binary sequence; According to the key and the chaotic mapping function, two chaotic sequences are generated, namely a first chaotic sequence and a second chaotic sequence; according to the first chaotic sequence and the preset characters, a Huffman tree is constructed; According to the second chaotic sequence and the Huffman tree, the plaintext binary sequence is encrypted to obtain a ciphertext sequence consisting of a plurality of ciphertext data, wherein the encryption step comprises: A plurality of combinations of all preset characters are obtained, wherein the combination includes a plurality of combinations, each combination includes any two preset characters, the two preset characters in the combination are combined characters of each other, and the preset characters between the plurality of combinations are not repeated; a target combination is selected from all the combinations according to the size of the second chaotic value in the second chaotic sequence; in the target combination, a combined character of the previous ciphertext data is obtained; a target child node corresponding to the combined character on the Huffman tree is obtained; another child node of the parent node of the target child node is used as a starting node; according to the order and value of the plaintext binary data in the plaintext binary sequence, a walk is started from the starting node on the Huffman tree until a termination node with the preset character is obtained; the preset character corresponding to the termination node is used as the ciphertext data; and the plaintext binary data contained in the walk path is deleted from the plaintext binary sequence. 2. According to the intelligent mine loading and transportation monitoring method described in claim 1, it is characterized in that the key is generated based on the parameters and initial conditions of the chaotic mapping system. 3. The intelligent mine loading and transportation monitoring method according to claim 1 is characterized in that the Huffman tree is constructed according to the first chaotic sequence and the preset characters, comprising: The cumulative sum of the first R first chaotic values in the first chaotic sequence is used as the first value, where R represents the number of preset characters; The ratio of the i-th first chaotic value to the first value in the first chaotic sequence is used as the preset frequency of the i-th preset character, where i takes all integers in the range of [1, R]; Construct a Huffman tree based on the preset frequencies of all preset characters. 4. The intelligent mine loading and transportation monitoring method according to claim 1 is characterized in that the target combination mode is selected from all combination modes according to the size of the second chaotic value in the second chaotic sequence, including: Record the number of combinations of all preset characters as the first number; The product of the second chaotic value in the second chaotic sequence and the first number is rounded to an integer, which is recorded as a second value S; The Sth combination among all the combinations is taken as the target combination. 5. The intelligent mine loading and transportation monitoring method according to claim 1 is characterized in that the encryption step further comprises: The encryption steps are repeated until the plaintext binary sequence is empty or a termination node with a preset character cannot be obtained, and the iteration is stopped. 6. The intelligent mine loading and transportation monitoring method according to claim 1, characterized in that the method further comprises: During the first encryption, the root node on the Huffman tree is used as the starting node. 7. The intelligent mine loading and transportation monitoring method according to claim 1, characterized in that the method further comprises: When the obtained starting node is a node having a preset character, the preset character corresponding to the starting node is used as ciphertext data. 8. The intelligent mine loading and transportation monitoring method according to claim 1, characterized in that the method further comprises: The ciphertext sequence is stored on the server; The preset characters and the key are stored on an offline device, which can only be used by personnel with access authority. 9. An intelligent mine loading and transportation monitoring system, characterized in that it includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent mine loading and transportation monitoring method according to any one of claims 1 to 8 is implemented.