Asset management system based on Internet of Things

Abstract

The invention discloses an asset management system based on the Internet of Things, and the system comprises an asset positioning tracking module (100) which transmits asset positioning data to an intelligent decision control module (300), and an environment state sensing module (200) which collects environment data, triggers an abnormal early warning threshold value of the intelligent decision control module (300), and generates a monitoring log. The intelligent decision control module (300) receives asset positioning data and environmental data; the data security management module (500) forms closed-loop security control according to the positioning precision of the asset positioning and tracking module (100) and the data calling authority of the environment state sensing module (200). Through modular design and intelligent fusion decision making, key indexes such as positioning precision, environment perception sensitivity, response speed and safety are all subjected to breakthrough improvement. The system can adapt to asset management requirements of different values and different sensitivities, and quantifiable economic benefits are formed.

Description

Technical Field

[0001] The present invention relates to the field of big data asset management, and in particular to an asset management system based on the Internet of Things. Background Art

[0002] Currently, with the rapid development of Internet of Things (IoT) technology, asset management systems are widely used in logistics, manufacturing, healthcare and other fields. However, traditional asset management systems still have the following technical defects:

[0003] Positioning and condition monitoring are disconnected: Existing systems often use independently operated positioning modules, such as GPS, RFID, and environmental sensors, lacking data fusion mechanisms. For example, asset location information cannot be linked to environmental parameters such as temperature, humidity, and vibration for analysis. This results in delayed responses to abnormal events and difficulty accurately assessing asset damage risks. For example, excessive vibration during transportation of precision instruments may go undetected.

[0004] Low level of intelligent decision-making: Most systems rely on preset thresholds to trigger alerts, failing to account for differences in asset types and dynamic environmental changes. For example, the same vibration threshold may apply to both general cargo and fragile items. This lacks the ability to adaptively adjust based on historical data and real-time scenarios, resulting in high rates of false alarms and missed alerts.

[0005] Weak data security and permission control: Traditional systems often use centralized databases to store operation logs, which poses a risk of data tampering. Furthermore, permission management relies on static accounts and passwords, making it impossible to dynamically adjust data access granularity based on user roles. For example, temporary personnel could access high-precision location information, leading to the potential leakage of sensitive information.

[0006] Lack of multimodal interaction: Existing systems primarily rely on fixed terminal operations for human-computer interaction, lacking the synergy between natural language control, visualization, and automated control. For example, in an emergency, operators cannot quickly intervene in equipment status through voice commands and must manually switch between multiple interfaces, delaying emergency response time.

[0007] Extensive energy efficiency management: IoT devices often suffer from insufficient battery life due to continuous high power consumption. Existing solutions often use fixed sampling frequencies and fail to dynamically optimize energy consumption based on asset usage scenarios. For example, the positioning module of stationary assets in a warehouse maintains high-frequency scanning, resulting in energy waste.

[0008] To address the above issues, the asset management system of the present invention achieves efficient management and safe control of the entire life cycle of assets through technological innovations such as multi-source data fusion, dynamic decision-making algorithms, blockchain evidence storage and cross-modal interaction. Summary of the Invention

[0009] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0010] In view of the above-mentioned and / or existing problems in an existing asset management system based on the Internet of Things, the present invention is proposed.

[0011] Therefore, the problem to be solved by the present invention is how to provide an asset management effect that can be achieved based on the Internet of Things.

[0012] In order to solve the above technical problems, the present invention provides the following technical solutions: an asset management system based on the Internet of Things, which includes:

[0013] Asset location tracking module, environmental status perception module, intelligent decision-making control module, remote operation terminal module and data security management module, each module realizes data interaction through encrypted communication links;

[0014] The electronic tag identification unit of the asset location tracking module works in conjunction with the multi-source positioning unit to package the asset binding information and real-time location coordinates into asset location data and transmit it to the intelligent decision control module, while also synchronizing asset identity authentication records to the data security management module;

[0015] The temperature and humidity sensor array and vibration monitoring unit of the environmental state perception module periodically collect environmental data, trigger the abnormal warning threshold of the intelligent decision control module after pre-processing by the edge computing node, and upload the encrypted data stream to the blockchain evidence storage unit to generate a monitoring log;

[0016] The intelligent decision control module receives the fusion input of asset location data and environmental data, and generates asset maintenance instructions through a dynamic weight distribution algorithm, wherein the first output port is connected to the asset execution mechanism to realize automatic control, and the second output port pushes visual alarm information and operation suggestions to the remote operation terminal module;

[0017] The visual operation interface of the remote operation terminal module displays the analysis results of the intelligent decision control module in real time. At the same time, the user instructions received by the voice control unit are verified by the authority classification unit, and the intelligent decision control module is reversely controlled to adjust the decision parameters;

[0018] The blockchain evidence storage unit of the data security management module adds digital fingerprints to the transmitted data between all modules, and the permission classification unit dynamically limits the positioning accuracy of the asset positioning tracking module and the data retrieval permission of the environmental status perception module according to the user role, forming a closed-loop security control.

[0019] As a preferred solution of the IoT-based asset management system described in the present invention, the multi-source positioning unit integrates a dual-mode positioning chip and a Bluetooth beacon receiver, and generates real-time asset location coordinates by fusing satellite positioning data and Bluetooth signals through a Kalman filter algorithm;

[0020] The dual-mode positioning includes GPS positioning and Beidou positioning.

[0021] As a preferred solution of the asset management system based on the Internet of Things described in the present invention, the vibration monitoring unit has a built-in three-axis accelerometer and is configured with an adaptive vibration threshold calculation model. When the vibration intensity is detected to be 3 times the standard deviation of the historical data, the environmental status perception module is triggered to send a third-level alarm signal to the intelligent decision-making control module.

[0022] As a preferred solution of the asset management system based on the Internet of Things described in the present invention, the dynamic weight allocation algorithm includes the following operations:

[0023] First, an asset type parameter matrix including temperature sensitivity coefficient, humidity impact factor, and vibration damage value is constructed;

[0024] Secondly, the weighted coefficient of each indicator is calculated according to the degree to which the environmental data deviates from the baseline value;

[0025] When the comprehensive evaluation value exceeds a preset threshold, a maintenance instruction is generated, and the instruction types include constant temperature control, vibration reduction reinforcement or shutdown maintenance.

[0026] As a preferred solution of the IoT-based asset management system described in the present invention, the voice control unit integrates a two-way voice interaction function and a built-in natural language processing engine. After verifying the user's identity through voiceprint recognition technology, the voice command is converted into a JSON format control message, which is then sent to the intelligent decision control module after being authorized by the authority classification unit.

[0027] The JSON is an encrypted message format.

[0028] As a preferred solution of the asset management system based on the Internet of Things described in the present invention, the blockchain evidence storage unit adopts a layered storage architecture, splits the asset operation log into three data segments: timestamp, operation type, and device fingerprint, calculates the hash value for each segment, and writes it into different nodes of the alliance chain.

[0029] As a preferred solution of the asset management system based on the Internet of Things described in the present invention, it also includes an energy consumption optimization module, which is connected to the communication equipment of each module, collects equipment energy consumption data through a dynamic power consumption monitoring unit, uses a reinforcement learning algorithm to optimize the scanning frequency of the positioning and tracking module and the sampling period of the environmental perception module, and controls the solar power supply switching timing of the backup power supply unit.

[0030] As a preferred solution of the asset management system based on the Internet of Things described in the present invention, the electronic tag identification unit adopts an active tag with a built-in anti-collision identification mechanism. When more than 50 unidentified tags are detected within a radius of 10 meters, it automatically switches to the time-sharing multiple access working mode and activates the tag power monitoring function, sending an early warning signal to the remote operation terminal module for tags with a remaining power of less than 20%.

[0031] In a second aspect, some embodiments of the present invention provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation method of the above-mentioned first aspect.

[0032] In a third aspect, some embodiments of the present invention provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any one of the implementations of the first aspect is implemented.

[0033] The beneficial effect of the present invention is to propose an asset management system based on the Internet of Things, which has the following significant advantages:

[0034] Multi-dimensional Data Fusion and Intelligent Decision-Making: The system innovatively integrates asset location tracking, environmental status perception, and historical data for dynamic correlation and analysis, achieving a deep coupling of physical space and digital information. Adaptive algorithms assess asset health in real time, combining environmental anomalies with asset type differences to automatically generate precise maintenance strategies. This significantly improves risk prediction and response efficiency, avoiding the misjudgments associated with traditional threshold alarm mechanisms.

[0035] Full-chain security and reliability: A security architecture that combines blockchain technology with dynamic permission management ensures traceability throughout the entire data collection, transmission, and storage process. Operation records are tamper-proofed through distributed evidence storage, while data access permissions are dynamically adjusted based on user roles. This effectively prevents the leakage of sensitive information and the risk of unauthorized operations while maintaining system flexibility.

[0036] Optimized human-machine collaborative interaction: The system integrates a visual interface, voice control, and automated control functions to create a multimodal interaction channel. Operators can view a real-time visual map of the asset's full-dimensional status and quickly intervene in equipment operations through natural language commands. This significantly shortens the decision-making process in emergency situations and achieves a seamless integration between manual operation and intelligent systems.

[0037] Comprehensively improve resource efficiency: Intelligent scheduling algorithms dynamically optimize device operating parameters, ensuring monitoring accuracy while reducing terminal energy consumption. The system automatically adjusts positioning frequency and environmental sampling intervals based on asset status, dynamically adapting power supply strategies based on energy supply characteristics, significantly extending the battery life of IoT devices and reducing operation and maintenance costs.

[0038] Scenario-based adaptive capabilities: The system's built-in flexible decision-making model can automatically match management and control strategies according to different industry scenarios. By continuously learning historical data and optimizing the weights of evaluation parameters, it enables asset management strategies to dynamically evolve and meet diverse needs in complex environments.

[0039] Through the above-mentioned technological breakthroughs, the present invention has achieved a leapfrog upgrade in asset management from passive monitoring to active early warning, from single function to system collaboration, and from experience-driven to data intelligence, providing an efficient, safe and sustainable solution for digital asset management in the Industrial 4.0 era. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] in:

[0043] Figure 1 This is a flow chart of an asset management system based on the Internet of Things in Example 1.

[0044] Figure 2 This is a schematic diagram of the asset management logic of an IoT-based asset management system in Example 1. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0046] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0047] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0048] Example 1

[0049] Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides an asset management system based on the Internet of Things, comprising:

[0050] The asset location tracking module 100, the environmental status perception module 200, the intelligent decision-making control module 300, the remote operation terminal module 400 and the data security management module 500 implement data exchange through encrypted communication links;

[0051] The electronic tag identification unit 101 of the asset location tracking module 100 works in conjunction with the multi-source location unit 102 to package the asset binding information and real-time location coordinates into asset location data and transmit it to the intelligent decision control module 300, while also synchronizing the asset identity authentication record to the data security management module 500;

[0052] The electronic tag identification unit 101 uses an active tag with a built-in anti-collision identification mechanism. When it detects that there are more than 50 unidentified tags within a radius of 10 meters, it automatically switches to the time-sharing multiple access working mode and activates the tag power monitoring function, sending an early warning signal to the remote operation terminal module 400 for tags with a remaining power of less than 20%.

[0053] The multi-source positioning unit 102 integrates a dual-mode positioning chip and a Bluetooth beacon receiver, and uses the Kalman filter algorithm to fuse satellite positioning data and Bluetooth signals to generate the real-time location coordinates of the asset;

[0054] Dual-mode positioning includes GPS positioning and Beidou positioning.

[0055] The temperature and humidity sensor array 201 and vibration monitoring unit 202 of the environmental state perception module 200 periodically collect environmental data, trigger the abnormal warning threshold of the intelligent decision control module 300 after pre-processing by the edge computing node, and upload the encrypted data stream to the blockchain evidence storage unit 501 to generate a monitoring log;

[0056] The vibration monitoring unit 202 has a built-in three-axis accelerometer and is configured with an adaptive vibration threshold calculation model. When the vibration intensity is detected to be 3 times greater than the standard deviation of the historical data, the environmental status perception module 200 is triggered to send a third-level alarm signal to the intelligent decision control module 300.

[0057] The intelligent decision-making control module 300 receives the fusion input of asset location data and environmental data, and generates asset maintenance instructions through a dynamic weight distribution algorithm. The first output port is connected to the asset execution mechanism to realize automatic control, and the second output port pushes visual alarm information and operation suggestions to the remote operation terminal module 400;

[0058] The dynamic weight allocation algorithm includes the following operations:

[0059] First, an asset type parameter matrix including temperature sensitivity coefficient, humidity impact factor, and vibration damage value is constructed;

[0060] Secondly, the weighted coefficient of each indicator is calculated according to the degree to which the environmental data deviates from the baseline value;

[0061] When the comprehensive evaluation value exceeds the preset threshold, a maintenance instruction is generated. The instruction types include constant temperature control, vibration reduction reinforcement or shutdown maintenance.

[0062] The visual operation interface 401 of the remote operation terminal module 400 displays the analysis results of the intelligent decision control module 300 in real time. At the same time, the user instructions received by the voice control unit 402 are verified by the authority classification unit 502, and then reversely control the intelligent decision control module 300 to adjust the decision parameters;

[0063] The voice control unit 402 integrates a two-way voice interaction function and a built-in natural language processing engine. After verifying the user's identity through voiceprint recognition technology, it converts the voice command into a JSON format control message, which is sent to the intelligent decision control module 300 after authorization by the authority classification unit 502. JSON is an encrypted message format.

[0064] The blockchain evidence storage unit 501 of the data security management module 500 adds digital fingerprints to the transmitted data between all modules, and the authority classification unit 502 dynamically limits the positioning accuracy of the asset positioning tracking module 100 and the data retrieval authority of the environmental status perception module 200 according to the user role, forming a closed-loop security control.

[0065] The blockchain evidence storage unit 501 adopts a layered storage architecture, splitting the asset operation log into three data segments: timestamp, operation type, and device fingerprint, and calculating the hash value for each segment and writing it into different nodes of the alliance chain.

[0066] Energy consumption optimization module 600, the energy consumption optimization module 600 is connected to the communication equipment of each module, collects equipment energy consumption data through the dynamic power consumption monitoring unit 601, uses the reinforcement learning algorithm to optimize the scanning frequency of the positioning tracking module 100 and the sampling period of the environmental perception module 200, and controls the solar power supply switching timing of the backup power supply unit 602.

[0067] Example 2

[0068] The second embodiment of the present invention is different from the first embodiment in that it also includes the following test preparation and implementation process:

[0069] This embodiment selects a precision instrument warehouse of a national laboratory as the test environment. The warehouse stores high-precision measuring equipment worth more than 230 million yuan and has strict requirements on the temperature, humidity, vibration and location management of the storage environment. The experiment compared the performance differences between the traditional asset management method and the system of the present invention. The test cycle was 90 consecutive days. The warehouse was divided into 6 test areas, and different types of precision instruments were deployed in each area, including electron microscopes (area A), atomic force microscopes (area B), laser interferometers (area C), spectrometers (area D), superconducting quantum devices (area E) and X-ray diffractometers (area F).

[0070] The experimental equipment configuration includes: the asset positioning and tracking module adopts UWB+Bluetooth 5.1 dual-mode positioning technology, deploys 42 positioning beacons to form a three-dimensional positioning network, and the positioning tags adopt IP67 protection level; the environmental status perception module contains 36 temperature and humidity sensors (accuracy ±0.3℃ / ±2%RH) and 18 three-axis vibration sensors (range ±5g, resolution 0.001g), distributed in a three-dimensional grid; the intelligent decision-making control module is equipped with an edge computing server, configured with an Intel Xeon 8-core processor and 32GB of memory; the data security management module uses the Hyperledger Fabric architecture to build a private blockchain network.

[0071] The implementation process was divided into three phases: The first phase (days 1-30) operated a traditional management system as a control, using RFID positioning (3-5 meter accuracy) and scheduled environmental inspections; the second phase (days 31-60) deployed the proposed system, but maintained the decision-making module in manual mode; and the third phase (days 61-90) enabled fully automated intelligent decision-making and control. During the testing period, various abnormal scenarios were simulated: artificial temperature and humidity fluctuations were created on days 15, 45, and 75; random vibration disturbances of 0.5-2.3g were introduced on days 22, 52, and 82; and the positions of 5-8 devices were randomly adjusted weekly to test tracking performance.

[0072] System parameter settings: Positioning data update frequency is set to 1Hz (mobile assets) / 0.2Hz (static assets); environmental sampling interval is 10 seconds; abnormal warning thresholds are dynamically adjusted based on device type, triggering an alert if temperature and humidity deviations exceed the set value by ±1.5°C / ±5%RH, or if vibration exceeds 0.2g for 10 seconds; the blockchain generates new blocks every 30 seconds, and data upload latency is controlled within 800ms. The maintenance command generation window is set to 5 minutes, and the system integrates data from the last 20 sampling periods for trend analysis.

[0073] Table 1: Comparative test results of asset positioning accuracy (unit: meters)

[0074]

[0075] Table 2: Comparison of environmental anomaly detection performance

[0076]

[0077] Table 3: Comparison of decision-making control effects

[0078]

[0079]

[0080] Table 4: Safety performance test results

[0081]

[0082] Table 5: Comparison of maintenance costs in different regions (unit: yuan / month)

[0083]

[0084] Table 6: Comparison of system resource usage (%)

[0085]

[0086] As shown in the positioning performance data in Table 1, the proposed system, through the collaborative operation of UWB and Bluetooth 5.1 multi-source positioning technologies, improves the positioning accuracy of static assets to 0.11-0.18 meters, approximately 20-30 times higher than traditional RFID systems. Even in dynamic tracking, the system maintains an accuracy of 0.23-0.31 meters, with a response time of less than 1.4 seconds, which is crucial for collision protection of precision instruments. Of particular note is the superconducting quantum device's positioning accuracy of 0.11 meters, demonstrating the effectiveness of the system's optimized positioning algorithm for sensitive equipment.

[0087] The environmental monitoring data presented in Table 2 reveals the value of edge computing preprocessing in this invention. The detection rate for temperature and humidity anomalies increased from 68% in traditional systems to 99.7%, while vibration detection improved by 76.9%, with an average early warning lead time of 14.2 minutes. This performance improvement stems from the sensor array's spatiotemporal correlation analysis and dynamic threshold adjustment mechanism, which enables the recognition rate of complex anomalies (such as simultaneous temperature and humidity fluctuations accompanied by slight vibrations) to leap from 41% to 95.2%, while maintaining a false alarm rate below 1.5%.

[0088] The decision-making and control results (Table 3) show that the fully automatic mode reduced abnormal response time from 38.5 minutes to 2.1 minutes, improving regulation accuracy by an order of magnitude. A dynamic weight allocation algorithm enables differentiated control strategies for different zones. For example, Zone E (superconducting quantum devices) receives the highest priority, with its environmental parameter fluctuations controlled within ±0.2°C / ±1%RH. The 23.7% energy savings are primarily due to the system's ability to intelligently adjust the operating power of environmental control devices based on their usage.

[0089] The significant improvement in security performance (Table 4) demonstrates the effectiveness of blockchain evidence storage and dynamic permission control. The 100% data tampering detection rate is achieved through a 30-second blockchain verification mechanism, while location spoofing prevention is achieved through digital fingerprinting and two-way authentication. Notably, data traceability has been reduced from over 60 minutes to under one minute, revolutionizing laboratory audits.

[0090] Economic benefit analysis (Table 5) shows a 36.1% reduction in average maintenance costs across all regions. Region E, with the highest equipment value, saw absolute savings of 4,200 yuan per month. Resource utilization optimization (Table 6) shows a 46.2% reduction in network bandwidth usage, thanks to edge computing nodes filtering 85% of raw data and only uploading feature data to the central server. Computing resource optimization by 37.6% stems from a dynamic load balancing algorithm that allocates computing power based on the real-time needs of each module.

[0091] Comprehensive test data demonstrates that this invention, through modular design and intelligent fusion decision-making, achieves breakthrough improvements in key metrics such as positioning accuracy, environmental perception sensitivity, response speed, and security. In particular, the multi-source data fusion and dynamic weight allocation strategy enable the system to adapt to the management needs of assets of varying value and sensitivity, generating quantifiable economic benefits. The innovative application of blockchain technology addresses the inherent shortcomings of traditional IoT systems in terms of audit traceability and data integrity, providing a trusted management environment for high-value assets.

[0092] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or components referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention; the terms "first", "second", and "third" are only used for descriptive purposes and should not be understood as indicating or implying relative importance. In addition, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, an indirect connection through an intermediate medium, or it can be internal communication between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An asset management system based on the Internet of Things, characterized in that: It includes an asset location tracking module (100), an environmental status perception module (200), an intelligent decision control module (300), a remote operation terminal module (400) and a data security management module (500), and each module realizes data interaction through an encrypted communication link; The electronic tag identification unit (101) of the asset location tracking module (100) is linked with the multi-source location unit (102) to package the asset binding information and the real-time location coordinates into asset location data and transmit it to the intelligent decision control module (300), while synchronizing the asset identity authentication record to the data security management module (500); The temperature and humidity sensor array (201) and the vibration monitoring unit (202) of the environmental state perception module (200) periodically collect environmental data, trigger the abnormal warning threshold of the intelligent decision control module (300) after pre-processing by the edge computing node, and upload the encrypted data stream to the blockchain evidence storage unit (501) to generate a monitoring log; The intelligent decision control module (300) receives a fusion input of asset location data and environmental data, and generates asset maintenance instructions through a dynamic weight distribution algorithm, wherein a first output port is connected to an asset execution mechanism to realize automatic control, and a second output port pushes visual alarm information and operation suggestions to a remote operation terminal module (400); The visual operation interface (401) of the remote operation terminal module (400) displays the analysis results of the intelligent decision control module (300) in real time, and at the same time, after the user instructions received by the voice control unit (402) are verified by the authority classification unit (502), the intelligent decision control module (300) is reversely controlled to adjust the decision parameters; The blockchain evidence storage unit (501) of the data security management module (500) adds digital fingerprints to the transmission data between all modules, and the authority classification unit (502) dynamically limits the positioning accuracy of the asset positioning tracking module (100) and the data retrieval authority of the environmental status perception module (200) according to the user role, thereby forming a closed-loop security control.

2. The asset management system based on the Internet of Things according to claim 1, characterized in that: The multi-source positioning unit (102) integrates a dual-mode positioning chip and a Bluetooth beacon receiver, and generates real-time asset location coordinates by fusing satellite positioning data and Bluetooth signals through a Kalman filter algorithm; The dual-mode positioning includes GPS positioning and Beidou positioning.

3. The asset management system based on the Internet of Things according to claim 1, characterized in that: The vibration monitoring unit (202) has a built-in three-axis accelerometer and is configured with an adaptive vibration threshold calculation model. When the vibration intensity is detected to be more than three times the standard deviation of historical data, the environmental state perception module (200) is triggered to send a third-level alarm signal to the intelligent decision control module (300).

4. The asset management system based on the Internet of Things according to claim 3, characterized in that: The dynamic weight allocation algorithm includes the following operations: First, an asset type parameter matrix including temperature sensitivity coefficient, humidity impact factor, and vibration damage value is constructed; Secondly, the weighted coefficient of each indicator is calculated according to the degree to which the environmental data deviates from the baseline value; When the comprehensive evaluation value exceeds a preset threshold, a maintenance instruction is generated, and the instruction types include constant temperature control, vibration reduction reinforcement or shutdown maintenance.

5. The asset management system based on the Internet of Things according to claim 1, characterized in that: The voice control unit (402) integrates a two-way voice interaction function and a built-in natural language processing engine. After verifying the user's identity through voiceprint recognition technology, the voice command is converted into a JSON format control message, which is then sent to the intelligent decision control module (300) after being authorized by the authority classification unit (502); The JSON is an encrypted message format.

6. The asset management system based on the Internet of Things according to claim 1, characterized in that: The blockchain evidence storage unit (501) adopts a layered storage architecture, splitting the asset operation log into three data segments: timestamp, operation type, and device fingerprint, and calculating hash values ​​for each segment and writing them into different nodes of the alliance chain.

7. The asset management system based on the Internet of Things according to claim 1, characterized in that: The system further comprises an energy consumption optimization module (600), which is connected to the communication devices of each module, collects device energy consumption data through a dynamic power consumption monitoring unit (601), optimizes the scanning frequency of the positioning tracking module (100) and the sampling period of the environment perception module (200) using a reinforcement learning algorithm, and controls the solar power supply switching timing of the backup power supply unit (602).

8. The asset management system based on the Internet of Things according to claim 1, characterized in that: The electronic tag identification unit (101) uses an active tag and has a built-in anti-collision identification mechanism. When it detects that there are more than 50 unidentified tags within a radius of 10 meters, it automatically switches to a time-sharing multiple access working mode and activates a tag power monitoring function. For tags with a remaining power of less than 20%, an early warning signal is sent to a remote operation terminal module (400).

9. An electronic device, characterized in that include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the system according to any one of claims 1 to 8.

10. A computer-readable storage medium having executable instructions stored thereon, characterized in that When the instructions are executed by a processor, the processor implements the system according to any one of claims 1 to 8.

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