Pipe gallery data management method and system based on AIOT management platform

Abstract

The invention discloses a pipe gallery data management method and system based on an AIOT management platform, pipe gallery data is managed through the AIOT management platform, the AIOT management platform is connected with a plurality of monitoring devices arranged in a pipe gallery, and the method comprises the following steps: carrying out regional division on the pipe gallery according to a preset rule to obtain a plurality of sub-regions, respectively establishing corresponding storage databases according to the divided sub-regions; monitoring equipment in the sub-region is determined, the monitoring equipment in the sub-region is used as an equipment cluster, and leader equipment is selected from the equipment cluster according to a preset rule; and according to the related information of the leader device, determining an encryption key of the data collected by the corresponding device cluster, and after the pipe gallery data collected by the device cluster is obtained, storing the pipe gallery data into the corresponding storage database by using the encryption key. The problem of low safety of pipe gallery data management in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipe gallery data management, and particularly relates to a pipe gallery data management method and system based on an AIOT management platform. BACKGROUND

[0002] With the acceleration of urbanization process, as an important part of urban infrastructure construction, the underground comprehensive pipe gallery has been widely constructed and promoted because it can centrally lay various municipal pipelines such as power, communication, gas, water supply and drainage, effectively solves the problems of 'road zipper' and'spider web in the air', and improves the urban space utilization efficiency and operation safety.

[0003] In the daily operation and maintenance management of the pipe gallery, data management plays a crucial role. The traditional pipe gallery data management directly stores the data in the pipe gallery into a large database, while the pipe gallery contains various types of data such as environmental monitoring data and structural safety data. On the one hand, the unified storage of data in a large database is not easy to find and manage, and the pipe gallery data involves safety and operation information, so the security is low without encryption measures. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a pipe gallery data management method and system based on an AIOT management platform, which aims to solve the problem of low security of pipe gallery data management in the prior art.

[0005] The present application is implemented as follows:

[0006] A pipe gallery data management method based on an AIOT management platform, which manages the pipe gallery data through an AIOT management platform, and the AIOT management platform is connected to a plurality of monitoring devices arranged in the pipe gallery. The method comprises the following steps:

[0007] The pipe gallery is divided into a plurality of sub-areas according to a preset rule, and a corresponding storage database is established according to the divided sub-areas;

[0008] The monitoring devices in the sub-area are determined and the monitoring devices in the sub-area are regarded as a device cluster, and a leader device is elected from the device cluster according to a preset rule;

[0009] According to the related information of the leader device, the encryption key of the data collected by the corresponding device cluster is determined, and after the pipe gallery data collected by the device cluster is obtained, the pipe gallery data is stored in the corresponding storage database by using the encryption key.

[0010] Further, the pipe gallery data management method based on the AIOT management platform comprises the following steps:

[0011] Obtaining the trend of the pipe gallery, and equally dividing the pipe gallery in the trend direction according to the trend of the pipe gallery at a preset interval to divide the pipe gallery into a plurality of sub-regions.

[0012] Further, the pipe gallery data management method based on the AIOT management platform, wherein the step of electing a leader device from the device cluster according to the preset rule comprises:

[0013] Collecting hardware parameters, communication quality and historical reliability of the monitoring device, and determining a score corresponding to the monitoring device according to the hardware parameters, communication quality and historical reliability of the monitoring device;

[0014] Selecting a target monitoring device with the highest score from the device cluster, and taking the target monitoring device as the leader device of the device cluster.

[0015] Further, the pipe gallery data management method based on the AIOT management platform, wherein the step of determining an encryption key of the data collected by the corresponding device cluster according to the relevant information of the leader device comprises:

[0016] Obtaining the unique identifier of the leader device and the time stamp when the leader device was elected, and performing hash calculation on the unique identifier of the leader device to obtain a first hash value;

[0017] Converting the first hash value into a target complex number, and determining an initial parameter and an iteration number of the Mandelbrot set iteration according to the target complex number and the time stamp, respectively;

[0018] Iterating the Mandelbrot set using the initial parameter, recording the last target value that does not diverge in the iteration process, and mapping the real part and the imaginary part of the target value to a preset interval, respectively, to obtain respective integers and combining them into a byte array;

[0019] Determining a hexadecimal string according to the byte array, and taking the string as the encryption key of the data collected by the device cluster.

[0020] Further, the pipe gallery data management method based on the AIOT management platform, wherein the step of determining a hexadecimal string according to the byte array comprises:

[0021] Converting the byte array into a hexadecimal string; or

[0022] Obtaining a preset fixed salt value, calculating the MD5 hash value of the fixed salt value, and selecting a preset byte element therefrom;

[0023] The byte array is XORed with one part of elements in the preset byte to obtain a perturbation factor, and the perturbation factor is spliced with another part of elements in the preset byte to obtain a complete key factor:

[0024] The complete key factor is converted into a hexadecimal string.

[0025] Further, the pipeline data management method based on the AIOT management platform, wherein the step of converting the first hash value into a corresponding target complex number includes:

[0026] A part of elements of the first hash value is converted into a real part, and another part of elements of the first hash value is converted into an imaginary part, and a corresponding target complex number is obtained by normalizing to a preset interval.

[0027] Further, the pipeline data management method based on the AIOT management platform, wherein the expression of the Mandelbrot set is:

[0028]

[0029] Wherein, c is an initial parameter.

[0030] Another object of the present application is to provide a pipeline data management system based on an AIOT management platform, which manages pipeline data through an AIOT management platform, and the AIOT management platform is connected to a plurality of monitoring devices arranged in the pipeline, and the system comprises:

[0031] The division module is used for dividing the pipeline into a plurality of sub-regions according to a preset rule, and a corresponding storage database is established according to the divided sub-regions;

[0032] The determination module is used for determining the monitoring devices in the sub-regions and taking the monitoring devices in the sub-regions as a device cluster, and electing a leader device from the device cluster according to a preset rule;

[0033] The management module is used for determining an encryption key of the data collected by the corresponding device cluster according to the related information of the leader device, and storing the pipeline data collected by the device cluster into the corresponding storage database by using the encryption key after obtaining the pipeline data collected by the device cluster.

[0034] Another object of the present application is to provide a readable storage medium, which stores a computer program, and the program is executed by a processor to realize the steps of the method according to any one of the above.

[0035] It is another object of the present application to provide an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the above described methods when executing the program.

[0036] The present application divides the pipe gallery into multiple sub-regions according to a preset rule, establishes a corresponding storage database according to each sub-region, determines the monitoring devices in the sub-region and takes the monitoring devices in the sub-region as a device cluster, elects a leader device from the device cluster according to a preset rule, determines an encryption key of the data collected by the device cluster according to the relevant information of the leader device, and stores the pipe gallery data collected by the device cluster into the corresponding storage database by using the encryption key, so that the data in different sub-regions can be stored in categories, which facilitates quick positioning and retrieval, and the data can be stored by using the encryption key, which prevents the data from being illegally acquired and tampered with and ensures the safe operation of the pipe gallery. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A flowchart of the pipe gallery data management method based on the AIOT management platform in the first embodiment of the present application;

[0038] Figure 2 A structural block diagram of the pipe gallery data management system based on the AIOT management platform in the third embodiment of the present application.

[0039] The following specific embodiments will further illustrate the present application in combination with the above-mentioned drawings. DETAILED DESCRIPTION

[0040] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The drawings show several embodiments of the present application. However, the present application can be realized in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0041] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can be a middle element. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or there can be a middle element. The terms "vertical", "horizontal", "left", "right", and similar expressions used herein are for illustrative purposes only.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the relevant listed types.

[0043] Example One

[0044] See also Figure 1 , shown is a pipeline corridor data management method based on the AIOT management platform in the first embodiment of the present invention, in which the pipeline corridor data is managed through an AIOT management platform, and the AIOT management platform is respectively connected to multiple monitoring devices deployed in the pipeline corridor. The method includes steps S10 to S12.

[0045] Step S10: Divide the pipe corridor into multiple sub-areas according to preset rules, and establish corresponding storage databases according to the divided sub-areas.

[0046] Among them, the tunnel data management method based on the AIoT management platform achieves efficient control of tunnel data by building an intelligent Internet of Things management system. Specifically, the AIoT management platform, as the core hub, can establish real-time connections with multiple monitoring devices such as temperature and humidity sensors, gas concentration detectors, pressure transmitters, and cameras deployed in the tunnel through wired or wireless communication technologies (such as 5G, LoRa, and WiFi), thereby achieving comprehensive collection and aggregation of data such as tunnel environmental parameters and equipment operating status.

[0047] To optimize the data management efficiency, the system divides the pipe gallery in space according to preset rules, which can be based on the physical structure of the pipe gallery (such as dividing into a sub-region every 200 meters), functional modules (such as power cabin, gas cabin, and comprehensive cabin as independent sub-regions), or risk levels (such as high leakage risk areas and ordinary monitoring areas). The overall pipe gallery is divided into multiple logically independent sub-regions. A dedicated storage database is established for each sub-region. The database can use a distributed storage architecture (such as HBase, Cassandra) or a relational database (such as MySQL). According to the data characteristics of the sub-region (such as real-time requirements, data format), the storage engine is flexibly selected to realize the classified storage of environmental monitoring data, equipment operation logs, maintenance records, and other information. For example, in the embodiment of the present application, the direction of the pipe gallery is obtained, and the pipe gallery is equally divided in the direction according to the preset interval to divide the pipe gallery into multiple sub-regions. Through this structured data management method, subsequent data query, analysis, and abnormal early warning are facilitated, and the data retrieval efficiency and storage resource utilization are improved, providing solid data support for the intelligent operation and maintenance of the pipe gallery.

[0048] Step S11, determine the monitoring devices in the sub-region and take the monitoring devices in the sub-region as a device cluster, and select a leader device from the device cluster according to a preset rule.

[0049] After the pipe gallery sub-region division is completed, the monitoring devices in each sub-region are managed in clusters to optimize data collection and collaboration efficiency. First, through the device discovery mechanism of the AIoT management platform (such as IP address scanning, RFID tag identification, or Zigbee network self-organization), all monitoring devices in each sub-region are automatically identified and bound, and are classified as a logical "device cluster". Then, a "leader device" is determined from the cluster according to a preset election rule, which can be based on device performance parameters (such as CPU computing power, memory capacity, battery endurance), communication quality (such as signal strength, network delay), or geographic location (such as being located at the center of the sub-region, being a communication hub node). For example, an edge computing gateway with strong computing power and stable signal is preferentially selected as the leader device, or a polling mechanism is used to let the devices in the cluster take turns to assume the leadership role in time sequence. For example, in the embodiment of the present application, the hardware parameters, communication quality, and historical reliability of the monitoring devices are collected, and the scores of the monitoring devices are determined according to the hardware parameters, communication quality, and historical reliability of the monitoring devices. The target monitoring device with the highest score is selected from the device cluster, and the target monitoring device is taken as the leader device of the device cluster. Specifically, the hardware parameters, communication quality, and historical reliability (failure rate) of the monitoring devices are scored according to the preset standard, so as to select the target monitoring device with the highest score.

[0050] Step S12, determine the encryption key of the data collected by the corresponding device cluster according to the relevant information of the leader device, and store the pipeline data collected by the device cluster into the corresponding storage database using the encryption key.

[0051] Wherein, when the device cluster elects the leader device according to the preset rule, the unique encryption key is generated by calculating the relevant unique information of the leader device, such as device hardware fingerprint (unique identifier obtained by physical unclonable function (PUF)) through a specific encryption algorithm (such as symmetric encryption algorithm AES, hash function SHA-256). The key is unique and dynamic, and is used for the encryption storage of the data generated by the corresponding device cluster. The encrypted data is transmitted to the storage database of the corresponding sub-region for storage.

[0052] In summary, the pipeline data management method based on the AIOT management platform in the above embodiments of the present application divides the pipeline into multiple sub-regions according to a preset rule, establishes corresponding storage databases according to the divided sub-regions, determines the monitoring devices in the sub-region and takes the monitoring devices in the sub-region as a device cluster, elects a leader device from the device cluster according to a preset rule, determines the encryption key of the data collected by the corresponding device cluster according to the relevant information of the leader device, and stores the pipeline data collected by the device cluster into the corresponding storage database using the encryption key. By dividing the pipeline by region and establishing corresponding storage databases, the data of different sub-regions can be stored in categories, which facilitates quick positioning and retrieval. Storing data using encryption keys can prevent data from being illegally obtained and tampered with, and ensure the safety of pipeline operation. The problems of difficult pipeline data searching and low safety in the prior art are solved.

[0053] Example Two

[0054] The present embodiment also proposes a pipeline data management method based on an AIOT management platform. The pipeline data management method based on the AIOT management platform proposed in the present embodiment is different from the pipeline data management method based on the AIOT management platform proposed in the first embodiment in that:

[0055] The step of determining the encryption key of the data collected by the corresponding device cluster according to the relevant information of the leader device includes:

[0056] Obtain the unique identifier of the leader device and the time stamp when the leader device is elected, and perform hash calculation on the unique identifier of the leader device to obtain a first hash value;

[0057] Perform complex conversion on the first hash value to obtain a corresponding target complex number, and determine the initial parameter and the iteration number of the Mandelbrot set iteration according to the target complex number and the time stamp, respectively.

[0058] iterating the Mandelbrot set with the initial parameter, recording the last non-divergent target value in the iteration process, mapping the real part and the imaginary part of the target value to the preset interval respectively to obtain respective integers and combining the integers into a byte array;

[0059] determining a hexadecimal string according to the byte array, and taking the string as an encryption key of data collected by the device cluster.

[0060] The generation process of the encryption key deeply integrates the uniqueness of the leader device, the time dynamics, and the complexity of fractal geometry, forming a key generation mechanism with randomness and attack resistance. Specifically, the unique identifier (such as MAC address, hardware serial number) of the leader device and its selected timestamp are obtained first, the unique identifier is input into a hash function (such as SHA-256) for calculation to obtain a first hash value (such as a 32-byte binary sequence) of fixed length, then the first hash value is used for complex conversion to determine the corresponding target complex number to determine the initial parameter of the Mandelbrot set iteration, and the iteration number is determined according to the timestamp, wherein the timestamp can be converted into a second value, and the iteration number is controlled within a preset range by taking the modulus of the second value to reduce the calculation loss, and finally a byte array is obtained by using the Mandelbrot set iteration to generate an encryption key.

[0061] Specifically, the expression of the Mandelbrot set is:

[0062]

[0063] wherein c is the initial parameter.

[0064] Specifically, the first hash value is divided into two parts, the first 16 bytes are converted into the real part of the complex number, and the last 16 bytes are converted into the imaginary part, and the real part and the imaginary part are mapped to the interval [-2, 2] respectively through normalization processing to obtain the initial parameter c of the Mandelbrot set iteration. At the same time, the timestamp is converted into the iteration number N for controlling the depth of iteration, and based on the iteration formula of the Mandelbrot set, N iterations are performed from n=0. During the iteration process, the modulus of the complex number z is monitored in real time, if ∣z∣>2 at a certain time, it is determined that the sequence diverges and the iteration is not continued; if the iteration is not divergent after N iterations, the target value of the result of the last iteration is recorded, then the real part and the imaginary part of the target value are mapped to the interval [0, 255] and rounded (such as real part 0.345x255≈88, imaginary part 0.678x255≈173), and combined into a byte array [88, 173].

[0065] To further enhance the security of the key, two key generation paths are provided: one is to directly convert the byte array into a hexadecimal string; the other is to introduce a preset fixed salt value, first calculate its MD5 hash value (such as the first 8 bytes are "12345678"), select part of the bytes (such as the first 4 bytes "1234") from it and perform XOR operation (such as 88 XOR 18 = 106, 173 XOR 52 = 225) with the byte array to obtain the disturbance factor [106, 225], and then splice the remaining bytes (such as the last 4 bytes "5678") of the salt value to obtain the complete key factor [106, 225, 56, 78], and finally convert it into a hexadecimal string to obtain the final encryption key.

[0066] This key generation method increases the complexity of the key space by means of the chaotic characteristics of the Mandelbrot set (small changes in the initial value will result in dramatic differences in the iteration results), and further resists collision attacks through salt disturbance. For example, even if the two leader devices have only 1 bit difference in the unique identifier, the real and imaginary parts of the generated hash value will be significantly different, and the key obtained after Mandelbrot iteration may be completely unrelated, thereby ensuring the uniqueness and security of the device cluster data encryption, and being suitable for the pipeline monitoring data encryption scene which has very high requirements for key dynamics and attack resistance.

[0067] In summary, the pipeline data management method based on the AIOT management platform in the above embodiments of the present application divides the pipeline into multiple sub-regions according to a preset rule, establishes a corresponding storage database for each sub-region, determines the monitoring devices in the sub-region and takes the monitoring devices in the sub-region as a device cluster, selects a leader device from the device cluster according to a preset rule, determines the encryption key of the data collected by the device cluster according to the relevant information of the leader device, and stores the pipeline data collected by the device cluster into the corresponding storage database using the encryption key. By dividing the pipeline by region and establishing a corresponding storage database, the data in different sub-regions can be stored in a classified manner, which facilitates quick positioning and retrieval. Storing data using an encryption key can prevent data from being illegally obtained and tampered with, and ensure the safety of pipeline operation. The problems of difficult pipeline data searching and low safety in the prior art are solved.

[0068] Example Three

[0069] Please refer to Figure 2 , which shows a pipeline data management system based on an AIOT management platform according to a third embodiment of the present application. The pipeline data is managed by an AIOT management platform, and the AIOT management platform is connected to multiple monitoring devices arranged in the pipeline. The system comprises:

[0070] The division module 100 is configured to divide the pipe gallery into a plurality of sub-regions according to a preset rule, and establish a corresponding storage database according to the divided sub-regions;

[0071] The determination module 200 is configured to determine the monitoring devices in the sub-region and take the monitoring devices in the sub-region as a device cluster, and select a leader device from the device cluster according to a preset rule;

[0072] The management module 300 is configured to determine an encryption key of the data collected by the corresponding device cluster according to the related information of the leader device, and store the pipe gallery data collected by the device cluster into the corresponding storage database by using the encryption key after the pipe gallery data is acquired.

[0073] The functions or operation steps realized when the above modules are executed are basically the same as those of the above method embodiments, and will not be described here again.

[0074] Example Four

[0075] Another aspect of the present application also provides a readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to realize the steps of the method according to any one of the above embodiments one to two.

[0076] Example Five

[0077] Another aspect of the present application also provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor realizes the steps of the method according to any one of the above embodiments one to two when executing the program.

[0078] The technical features of the above embodiments can be combined in any manner, and to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0079] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of executable instructions for realizing the logic function, which can be embodied in any computer readable medium for use by or in conjunction with an instruction execution system, device or apparatus, such as a computer-based system, a system including a processor or other system that can fetch and execute instructions from an instruction execution system, device or apparatus. For the present specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in conjunction with an instruction execution system, device or apparatus, or in conjunction with these instruction execution systems, devices or apparatus.

[0080] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that is then employable by a computer. In some embodiments, the example computer-readable storage medium can be non-transitory. For example, in some embodiments, the example computer-readable storage medium can be a non-transitory medium.

[0081] It should be understood that aspects of the application can be implemented in hardware, software, firmware, or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques, which are well known in the art, can be used to implement the application: a hybrid of the above techniques, discrete logic circuit(s) having logic gates for implementing logic functions upon request pins of the discrete logic circuit(s), programmable logic array(s) (PLAs), field programmable gate array(s) (FPGAs), etc.

[0082] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the specification, the illustrative description 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 appropriate manner in one or more embodiments or examples.

[0083] The above-described embodiments are merely some embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the patent of the present application. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A pipeline corridor data management method based on AIOT management platform, characterized in that: The pipeline corridor data is managed through an AIOT management platform, which is connected to multiple monitoring devices deployed in the pipeline corridor. The method includes: The pipe corridor is divided into multiple sub-areas according to preset rules, and corresponding storage databases are established according to the divided sub-areas; Identify the monitoring devices in the sub-area and treat them as a device cluster. Select a leader device from the device cluster according to preset rules. The encryption key of the data collected by the corresponding equipment cluster is determined based on the relevant information of the leading equipment. After the corridor data collected by the equipment cluster is obtained, the corridor data is stored in the corresponding storage database using the encryption key.

2. The pipeline corridor data management method based on the AIOT management platform according to claim 1 is characterized in that: The step of dividing the pipe gallery into multiple sub-areas according to preset rules includes: The direction of the pipeline corridor is obtained, and the pipeline corridor is divided into equal intervals in the direction of the direction according to the preset spacing, so as to divide the pipeline corridor into multiple sub-areas.

3. The pipeline corridor data management method based on the AIOT management platform according to claim 1 is characterized in that: The step of selecting a leader device from the device cluster according to a preset rule includes: Collect the hardware parameters, communication quality, and historical reliability of the monitoring equipment, and determine the corresponding score of the monitoring equipment based on the hardware parameters, communication quality, and historical reliability of the monitoring equipment; The target monitoring device with the highest score is selected from the device cluster, and the target monitoring device is used as the leading device of the device cluster.

4. The pipeline corridor data management method based on the AIOT management platform according to claim 1 is characterized in that: The steps of determining the encryption key of data collected by the corresponding device cluster based on the relevant information of the leader device include: Obtaining a unique identifier of a leader device and a timestamp of when the leader device was elected, and performing a hash calculation on the unique identifier of the leader device to obtain a first hash value; Performing complex conversion on the first hash value to obtain a corresponding target complex number, and determining the initial parameters and the number of iterations of the Mandelbrot set according to the target complex number and the timestamp; Iterate the Mandelbrot set using the initial parameters, record the last undiverged target value during the iteration process, map the real and imaginary parts of the target value to the preset intervals to obtain their respective integers and combine them into a byte array; A hexadecimal string is determined according to the byte array, and the string is used as an encryption key for data collected by the device cluster.

5. The pipeline corridor data management method based on the AIOT management platform according to claim 4 is characterized in that: The step of determining a hexadecimal string according to the byte array comprises: Convert the byte array to a hexadecimal string; or Get a preset fixed salt value, calculate the MD5 hash value of the fixed salt value and select the element of the preset bytes from it; Perform an XOR operation on the byte array and some of the elements in the preset bytes to obtain the perturbation factor, and then concatenate the perturbation factor with another part of the elements in the preset bytes to obtain the complete key factor: Convert the full key factor to a hexadecimal string.

6. The pipeline corridor data management method based on the AIOT management platform according to claim 4 is characterized in that: The step of converting the first Hash value into a complex number to obtain a corresponding target complex number includes: A part of the elements of the first hash value is converted into a real part, another part of the elements of the first hash value is converted into an imaginary part, and the parts are normalized to a preset interval to obtain a corresponding target complex number.

7. The pipeline corridor data management method based on the AIOT management platform according to claim 4 is characterized in that: The expression of the Mandelbrot set is: Among them, c is the initial parameter.

8. A pipe gallery data management system based on the AIOT management platform, characterized in that: The pipeline corridor data is managed through an AIOT management platform, which is connected to multiple monitoring devices deployed in the pipeline corridor. The system includes: The division module is used to divide the pipe corridor into multiple sub-areas according to preset rules, and establish corresponding storage databases according to the divided sub-areas; A determination module is used to determine the monitoring devices in the sub-area and treat the monitoring devices in the sub-area as a device cluster, and select a leader device from the device cluster according to preset rules; The management module is used to determine the encryption key of the data collected by the corresponding equipment cluster based on the relevant information of the leading equipment. After obtaining the corridor data collected by the equipment cluster, the corridor data is stored in the corresponding storage database using the encryption key.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the program.

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