A pipeline gallery data management method and system based on an AIOT management platform

Abstract

This invention discloses a method and system for managing utility tunnel data based on an AIOT management platform. The AIOT management platform connects to multiple monitoring devices deployed within the utility tunnel. The method includes: dividing the utility tunnel into multiple sub-regions according to preset rules; establishing corresponding storage databases for each sub-region; identifying the monitoring devices within each sub-region and treating them as a device cluster; electing a leader device from the device cluster according to preset rules; determining the encryption key for the data collected by the corresponding device cluster based on the leader device's information; and storing the utility tunnel data collected by the device cluster in the corresponding storage database using the encryption key after acquiring the data. This invention solves the problem of low security in existing utility tunnel data management technologies.

Description

Technical Field

[0001] This invention relates to the field of utility tunnel data management technology, and in particular to a utility tunnel data management method and system based on an AIoT management platform. Background Technology

[0002] With the accelerated pace of urbanization, underground utility tunnels, as an important component of urban infrastructure construction, have been widely constructed and promoted because they can centrally lay various municipal pipelines such as electricity, communication, gas, water supply and drainage, effectively solving problems such as "road zipper" and "aerial spider web" in cities, and improving the efficiency of urban space utilization and operational safety.

[0003] Data management plays a crucial role in the daily operation and maintenance of utility tunnels. Traditional utility tunnel data management stores all data within the tunnel directly into a large database. However, utility tunnels contain various types of data, such as environmental monitoring data and structural safety data. Storing all data in one large database makes it difficult to find and manage, and since utility tunnel data involves safety and operational information, its security is low without encryption measures. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method and system for managing utility tunnel data based on an AIoT management platform, which aims to solve the problem of low security in utility tunnel data management in the prior art.

[0005] This invention is implemented as follows:

[0006] A method for managing utility tunnel data based on an AIoT management platform, wherein the AIoT management platform manages the utility tunnel data, and the AIoT management platform is connected to multiple monitoring devices deployed in the utility tunnel. The method includes:

[0007] The utility tunnel is divided into multiple sub-regions according to preset rules, and a corresponding storage database is established for each sub-region.

[0008] The monitoring devices within a sub-region are identified and grouped into a cluster. A leader device is then elected from the cluster according to preset rules.

[0009] Based on the relevant information of the leadership equipment, determine the encryption key for the data collected by the corresponding equipment cluster. After obtaining the pipeline data collected by the equipment cluster, store the pipeline data in the corresponding storage database using the encryption key.

[0010] Furthermore, in the aforementioned method for managing utility tunnel data based on an AIoT management platform, the step of dividing the utility tunnel into multiple sub-regions according to preset rules includes:

[0011] The direction of the utility tunnel is obtained, and the tunnel is divided into multiple sub-regions by dividing it into regions according to a preset spacing.

[0012] Furthermore, in the aforementioned method for managing utility tunnel data based on an AIoT management platform, the step of electing a leader device from the device cluster according to preset rules includes:

[0013] 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;

[0014] Select the target monitoring device with the highest score from the device cluster and designate it as the leader device of the device cluster.

[0015] Furthermore, in the aforementioned method for managing utility tunnel data based on an AIoT management platform, the step of determining the encryption key for the data collected by the corresponding equipment cluster based on the relevant information of the monitoring equipment includes:

[0016] Obtain the unique identifier of the leader device and the timestamp of the leader device's election; perform a hash calculation on the unique identifier of the leader device to obtain the first hash value.

[0017] The first hash value is transformed into a complex number to obtain the corresponding target complex number. The initial parameters and number of iterations for the Mandelbrot set iteration are determined based on the target complex number and the timestamp, respectively.

[0018] The Mandelbrot set is iterated using initial parameters. The last non-divergent target value during the iteration process is recorded. The real and imaginary parts of the target value are mapped to preset intervals to obtain their respective integers and combined into a byte array.

[0019] A hexadecimal string is determined based on the byte array, and this string is used as the encryption key for the data collected by the device cluster.

[0020] Furthermore, in the aforementioned method for managing utility tunnel data based on an AIoT management platform, the step of determining a hexadecimal string based on a byte array includes:

[0021] Convert the byte array to a hexadecimal string; or

[0022] Get the preset fixed salt value, calculate the MD5 hash value of the fixed salt value, and select the preset byte element from it;

[0023] The perturbation factor is obtained by XORing the byte array with a portion of the preset bytes, and then concatenating the perturbation factor with the other portion of the preset bytes to obtain the complete key factor.

[0024] Convert the complete key factor to a hexadecimal string.

[0025] Furthermore, in the above-mentioned method for managing utility tunnel data based on an AIoT management platform, the step of performing a complex number transformation on the first hash value to obtain the corresponding target complex number includes:

[0026] Convert one part of the elements of the first hash value to the real part, convert the other part of the elements of the first hash value to the imaginary part, and normalize them to the preset interval to obtain the corresponding target complex number.

[0027] Furthermore, in the above-mentioned method for managing utility tunnel data based on an AIoT management platform, the expression for the Mandelbrot set is:

[0028]

[0029] Where c is the initial parameter.

[0030] Another objective of this invention is to provide a utility tunnel data management system based on an AIoT management platform. This system manages utility tunnel data through an AIoT management platform, which connects to multiple monitoring devices deployed within the utility tunnel. The system includes:

[0031] The partitioning module is used to divide the utility tunnel into multiple sub-regions according to preset rules, and to establish corresponding storage databases for each sub-region.

[0032] The 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 elect a leader device from the device cluster according to preset rules;

[0033] The management module is used to determine the encryption key for the data collected by the corresponding equipment cluster based on the relevant information of the leadership equipment. After obtaining the pipeline data collected by the equipment cluster, the pipeline data is stored in the corresponding storage database using the encryption key.

[0034] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method as described in any of the preceding claims.

[0035] Another object of the present invention is to provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of any of the methods described above.

[0036] This invention divides the utility tunnel into multiple sub-regions according to preset rules, and establishes corresponding storage databases for each sub-region. It identifies the monitoring equipment within each sub-region and groups these devices into a cluster. A leader device is elected from this cluster according to preset rules. An encryption key for the data collected by the leader device is determined based on its information. After acquiring the utility tunnel data collected by the cluster, the data is stored in the corresponding storage database using the encryption key. By dividing the utility tunnel into regions and establishing corresponding storage databases, data from different sub-regions can be categorized and stored, facilitating rapid location and retrieval. Using encryption keys prevents unauthorized access and tampering, ensuring the safe operation of the utility tunnel. This invention solves the problems of difficult data retrieval and low security in existing technologies. Attached Figure Description

[0037] Figure 1 This is a flowchart of the utility tunnel data management method based on an AIoT management platform in the first embodiment of the present invention;

[0038] Figure 2 This is a structural block diagram of the utility tunnel data management system based on the AIoT management platform in the third embodiment of the present invention.

[0039] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0040] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0041] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed types.

[0043] Example 1

[0044] Please see Figure 1 The figure shows a method for managing utility tunnel data based on an AIOT management platform in the first embodiment of the present invention. The utility tunnel data is managed through an AIOT management platform, which is connected to multiple monitoring devices deployed in the utility tunnel. The method includes steps S10 to S12.

[0045] Step S10: Divide the utility tunnel into multiple sub-regions according to preset rules, and establish corresponding storage databases for each sub-region.

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

[0047] To optimize data management efficiency, the system spatially divides the utility tunnel according to preset rules. These rules can be based on the tunnel's physical structure (e.g., dividing it into sub-regions every 200 meters), functional modules (e.g., power compartments, gas compartments, and integrated compartments as independent sub-regions), or risk level (e.g., high-leakage risk areas and ordinary monitoring areas), dividing the entire utility tunnel into multiple logically independent sub-regions. Each sub-region corresponds to a dedicated storage database. The database can adopt a distributed storage architecture (e.g., HBase, Cassandra) or a relational database (e.g., MySQL). The storage engine is flexibly selected based on the sub-region's data characteristics (e.g., real-time requirements, data format), enabling the categorized storage of environmental monitoring data, equipment operation logs, maintenance records, and other information. For example, in an embodiment of the invention, the tunnel's orientation is obtained, and the tunnel is equidistantly divided along its orientation at preset intervals to divide the tunnel into multiple sub-regions. This structured data management approach facilitates subsequent data querying, analysis, and anomaly warnings, while improving data retrieval efficiency and storage resource utilization, providing solid data support for the intelligent operation and maintenance of the utility tunnel.

[0048] Step S11: Determine the monitoring devices within the sub-region and group the monitoring devices within the sub-region into a device cluster, and elect a leader device from the device cluster according to preset rules.

[0049] After the utility tunnel sub-areas are divided, the monitoring equipment within each sub-area is managed in a cluster to optimize data collection and collaboration efficiency. First, the device discovery mechanism of the AIoT management platform (such as IP address scanning, RFID tag identification, or Zigbee network self-organizing) automatically identifies and binds all monitoring equipment within each sub-area, grouping them into a logical "device cluster." Then, a "leader device" is determined from the cluster according to preset election rules. These rules can be based on device performance parameters (such as CPU computing power, memory capacity, and battery life), communication quality (such as signal strength and network latency), or geographical location (such as being located in the center of the sub-area or 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 allows devices within the cluster to take turns assuming the leader role in chronological order. For example, in this embodiment of the invention, the hardware parameters, communication quality, and historical reliability of the monitoring equipment are collected, and the score corresponding to the monitoring equipment is determined based on the hardware parameters, communication quality, and historical reliability of the monitoring equipment. The target monitoring equipment with the highest score is selected from the equipment cluster and is used as the leader equipment of the equipment cluster. Specifically, the hardware parameters, communication quality, and historical reliability (failure rate) of the monitoring equipment are scored according to preset standards to obtain the corresponding score, thereby electing the target monitoring equipment with the highest score.

[0050] Step S12: Determine the encryption key for the data collected by the corresponding equipment cluster based on the relevant information of the leadership equipment. After obtaining the pipe gallery data collected by the equipment cluster, store the pipe gallery data in the corresponding storage database using the encryption key.

[0051] Once a leader device is elected by the device cluster according to preset rules, a unique encryption key is generated based on the leader device's unique information, such as its hardware fingerprint (a unique identifier obtained through the Physically Unclonable Function, PUF), and a specific encryption algorithm (such as the symmetric encryption algorithm AES or the hash function SHA-256). This key is unique and dynamic, and is used for the encrypted storage of data generated by the corresponding device cluster. The encrypted data is then directed to the storage database of the corresponding sub-region for storage.

[0052] In summary, the utility tunnel data management method based on an AIoT management platform in the above embodiments of the present invention divides the utility tunnel into multiple sub-regions according to preset rules, and establishes corresponding storage databases for each sub-region. It identifies the monitoring devices within each sub-region and groups these devices as a single device cluster, electing a leader device from the cluster according to preset rules. Based on the leader device's information, it determines the encryption key for the data collected by the corresponding device cluster. After acquiring the utility tunnel data collected by the device cluster, it stores the data in the corresponding storage database using the encryption key. By dividing the utility tunnel into regions and establishing corresponding storage databases, data from different sub-regions can be categorized and stored, facilitating rapid location and retrieval. Using encryption keys to store data prevents unauthorized access and tampering, ensuring the safe operation of the utility tunnel. This solves the problems of difficult data retrieval and low security in existing technologies.

[0053] Example 2

[0054] This embodiment also proposes a utility tunnel data management method based on an AIoT management platform. The difference between the utility tunnel data management method based on an AIoT management platform proposed in this embodiment and the utility tunnel data management method based on an AIoT management platform proposed in Embodiment 1 is as follows:

[0055] The steps for determining the encryption key for the data collected by the corresponding device cluster based on the relevant information of the leader device include:

[0056] Obtain the unique identifier of the leader device and the timestamp of the leader device's election; perform a hash calculation on the unique identifier of the leader device to obtain the first hash value.

[0057] The first hash value is transformed into a complex number to obtain the corresponding target complex number. The initial parameters and number of iterations for the Mandelbrot set iteration are determined based on the target complex number and the timestamp, respectively.

[0058] The Mandelbrot set is iterated using initial parameters. The last non-divergent target value during the iteration process is recorded. The real and imaginary parts of the target value are mapped to preset intervals to obtain their respective integers and combined into a byte array.

[0059] A hexadecimal string is determined based on the byte array, and this string is used as the encryption key for the data collected by the device cluster.

[0060] The encryption key generation process deeply integrates the unique identifier of the leader device, the dynamic nature of time, and the complexity of fractal geometry, forming a key generation mechanism that combines randomness and attack resistance. Specifically, the unique identifier of the leader device (such as MAC address or hardware serial number) and its selected timestamp are first obtained. The unique identifier is input into a hash function (such as SHA-256) for calculation to obtain a first hash value of fixed length (such as a 32-byte binary sequence). Then, the first hash value is used to perform a complex number transformation to obtain the corresponding target complex number to determine the initial parameters of the Mandelbrot set iteration. The number of iterations is determined based on the timestamp. The timestamp can be converted into a second value, and then the second value is moduloed to control the number of iterations within a preset range to reduce computational overhead. Finally, a byte array is obtained by iterating through the Mandelbrot set to generate the encryption key.

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

[0062]

[0063] Where c is the initial parameter.

[0064] For example, the first hash value is split 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. Normalization is then applied to map the real and imaginary parts to the interval [-2, 2], yielding the initial parameter c for the Mandelbrot set iteration. Simultaneously, the timestamp is converted into the iteration count N to control the iteration depth. Based on the Mandelbrot set iteration formula, N iterations are performed starting 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 moment, the sequence is considered divergent, and iteration is stopped. If the sequence has not diverged after N iterations, the target value of the last iteration is recorded. Subsequently, the real and imaginary parts of the target value are mapped to the interval [0, 255] and rounded (e.g., real part 0.345 × 255 ≈ 88, imaginary part 0.678 × 255 ≈ 173), and combined into a byte array [88, 173].

[0065] To further enhance key security, 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 (e.g., the first 8 bytes are "12345678"), select a portion of bytes (e.g., the first 4 bytes "1234") and perform an XOR operation with the byte array (e.g., 88⊕18=106, 173⊕52=225) to obtain the perturbation factor [106,225], and then concatenate it with the remaining bytes of the salt value (e.g., the last 4 bytes "5678") to form 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 leverages the chaotic nature of Mandelbrot sets (small changes in initial values ​​can lead to drastically different iteration results) to increase the complexity of the key space, while further resisting collision attacks through salt perturbation. For example, even if two leader devices differ by only one bit in their unique identifier, the real and imaginary parts of the generated hash values ​​will be significantly different. The keys obtained after Mandelbrot iteration may be completely unrelated, thus ensuring the uniqueness and security of the device cluster data encryption. This method is suitable for pipeline monitoring data encryption scenarios with extremely high requirements for key dynamism and attack resistance.

[0067] In summary, the utility tunnel data management method based on an AIoT management platform in the above embodiments of the present invention divides the utility tunnel into multiple sub-regions according to preset rules, and establishes corresponding storage databases for each sub-region. It identifies the monitoring devices within each sub-region and groups these devices as a single device cluster, electing a leader device from the cluster according to preset rules. Based on the leader device's information, it determines the encryption key for the data collected by the corresponding device cluster. After acquiring the utility tunnel data collected by the device cluster, it stores the data in the corresponding storage database using the encryption key. By dividing the utility tunnel into regions and establishing corresponding storage databases, data from different sub-regions can be categorized and stored, facilitating rapid location and retrieval. Using encryption keys to store data prevents unauthorized access and tampering, ensuring the safe operation of the utility tunnel. This solves the problems of difficult data retrieval and low security in existing technologies.

[0068] Example 3

[0069] Please see Figure 2 The figure shows a utility tunnel data management system based on an AIOT management platform proposed in the third embodiment of the present invention. The system manages utility tunnel data through an AIOT management platform, which connects to multiple monitoring devices deployed within the utility tunnel. The system includes:

[0070] The partitioning module 100 is used to divide the pipe gallery into multiple sub-regions according to preset rules, and to establish corresponding storage databases for each sub-region.

[0071] The determination module 200 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 elect a leader device from the device cluster according to preset rules;

[0072] The management module 300 is used to determine the encryption key for the data collected by the corresponding equipment cluster based on the relevant information of the leadership equipment. After obtaining the pipeline data collected by the equipment cluster, the pipeline data is stored in the corresponding storage database using the encryption key.

[0073] The functions or operation steps implemented by the above modules are largely the same as those in the above method embodiments, and will not be repeated here.

[0074] Example 4

[0075] In another aspect, the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method described in any one of the above embodiments one to two.

[0076] Example 5

[0077] In another aspect, the present invention provides an electronic device, the electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any one of the methods described in embodiments one to two above.

[0078] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0080] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0081] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0082] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0083] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for managing utility tunnel data based on an AIoT management platform, characterized in that, The method involves managing utility tunnel data through an AIOT management platform, which connects to multiple monitoring devices deployed within the utility tunnel. The utility tunnel is divided into multiple sub-regions according to preset rules, and a corresponding storage database is established for each sub-region. The monitoring devices within a sub-region are identified and grouped into a cluster. A leader device is then elected from the cluster according to preset rules. Based on the relevant information of the leading equipment, the encryption key for the data collected by the corresponding equipment cluster is determined. After obtaining the pipe gallery data collected by the equipment cluster, the pipe gallery data is stored in the corresponding storage database using the encryption key. Specifically, this includes: Obtain the unique identifier of the leader device and the timestamp of the leader device's election; perform a hash calculation on the unique identifier of the leader device to obtain the first hash value. The first hash value is transformed into a complex number to obtain the corresponding target complex number. The initial parameters and number of iterations of the Mandelbrot set are determined based on the target complex number and the timestamp, respectively. The timestamp is converted into a second value, and then the second value is moduloed to control the number of iterations within a preset range. The Mandelbrot set is iterated using initial parameters. The last non-divergent target value during the iteration process is recorded. The real and imaginary parts of the target value are mapped to preset intervals to obtain their respective integers and combined into a byte array. Get the preset fixed salt value, calculate the MD5 hash value of the fixed salt value, and select the preset byte element from it; The perturbation factor is obtained by XORing the byte array with a portion of the preset bytes, and then concatenating the perturbation factor with the other portion of the preset bytes to obtain the complete key factor. Convert the complete key factor to a hexadecimal string; A hexadecimal string is determined based on the byte array, and this string is used as the encryption key for the data collected by the device cluster. 2.The pipeline data management method based on the AIOT management platform according to claim 1, characterized in that, The step of dividing the utility tunnel into multiple sub-regions according to preset rules includes: The direction of the utility tunnel is obtained, and the tunnel is divided into multiple sub-regions by dividing it into regions according to a preset spacing. 3.The pipeline data management method based on the AIOT management platform of claim 1, wherein, The step of electing a leader device from the device cluster according to preset rules 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; Select the target monitoring device with the highest score from the device cluster and designate it as the leader device of the device cluster.

4. The pipeline data management method based on the AIOT management platform according to claim 1, characterized in that, The step of performing a complex number transformation on the first hash value to obtain the corresponding target complex number includes: Convert one part of the elements of the first hash value to the real part, convert the other part of the elements of the first hash value to the imaginary part, and normalize them to the preset interval to obtain the corresponding target complex number.

5. The method for managing utility tunnel data based on an AIoT management platform according to claim 1, characterized in that, The expression for the Mandelbrot set is: Where c is the initial parameter.

6. A pipeline gallery data management system based on an AIOT management platform, characterized in that, The system manages utility tunnel data through an AIOT management platform, which connects to multiple monitoring devices deployed within the utility tunnel. This platform is used to implement the utility tunnel data management method based on the AIOT management platform as described in any one of claims 1 to 5. The system includes: The partitioning module is used to divide the utility tunnel into multiple sub-regions according to preset rules, and to establish corresponding storage databases for each sub-region. The 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 elect a leader device from the device cluster according to preset rules; The management module is used to determine the encryption key for the data collected by the corresponding equipment cluster based on the relevant information of the leadership equipment. After obtaining the pipeline data collected by the equipment cluster, the pipeline data is stored in the corresponding storage database using the encryption key.

7. A readable storage medium, having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 5.

8. An electronic device, comprising: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method as described in any one of claims 1 to 5.

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