Warehouse logistics remote monitoring system for smart park electricity consumption information acquisition equipment

Abstract

The invention discloses a smart park electricity consumption information acquisition equipment warehouse logistics remote monitoring system, relates to Internet of Things remote data monitoring, and solves the problems that in the prior art, the electricity consumption information acquisition equipment warehouse logistics remote monitoring capability is lagged, the data communication efficiency is low, and the data processing capability is poor. The system comprises an intelligent sensor module, a video monitoring module, a central server module, a remote control module, a network security module, an artificial intelligence module, a risk early warning module, a system integration and interconnection module and a privacy protection module. The remote control module provides a remote control function through the logistics monitoring cloud platform, and the logistics monitoring cloud platform remotely monitors and controls the electric information acquisition equipment and the warehouse logistics equipment through a remote communication protocol and a security access method; according to the invention, the warehouse logistics remote monitoring capability of the smart park electricity consumption information acquisition equipment is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote data monitoring of the Internet of Things, and more specifically to a remote monitoring system for warehousing and logistics of electricity consumption information collection equipment in a smart park. Background Art

[0002] The remote monitoring system of warehousing and logistics of power consumption information collection equipment in smart parks came into being in the context of today's digital age. With the rapid development of information technology and the rise of intelligent applications, enterprises and parks are facing the need for more efficient and intelligent warehousing and logistics management. Traditional logistics management methods have problems such as information opacity, inefficiency, and waste of resources, and cannot meet the requirements of modern logistics operations. Therefore, the remote monitoring system of warehousing and logistics of power consumption information collection equipment in smart parks came into being. With the support of existing technologies, the remote monitoring system of warehousing and logistics of power consumption information collection equipment in smart parks has been realized. The widespread application of Internet of Things technology enables various types of equipment and sensors to be connected to each other and realize real-time data collection and sharing. The development of cloud computing and big data analysis technology provides strong support for data processing and management. At the same time, the advancement of artificial intelligence technology enables the system to automatically monitor, warn and optimize.

[0003] However, the current remote monitoring system for storage and logistics of electricity consumption information collection equipment in smart parks still has some shortcomings. First, the traditional system has security risks in the process of data transmission and storage, and is vulnerable to hacker attacks and information leakage. In addition, the scheduling process of the traditional system mainly relies on manual experience, lacks the support of intelligent scheduling optimization algorithms, and has low efficiency. Secondly, the traditional system cannot predict and warn risks through historical data and real-time data, and fails to provide timely advice and decision support. In addition, the lack of effective integration and interconnection between the various modules of the traditional system leads to information island problems, affecting the overall management efficiency. Finally, the traditional system does not adequately protect personal sensitive information, and lacks privacy data desensitization and permission management mechanisms.

[0004] Therefore, in order to solve the shortcomings and challenges of the existing data monitoring system based on the industrial Internet platform, such as insufficient security, low scheduling efficiency, untimely risk warning, information islands and privacy security, the present invention discloses a remote monitoring system for warehousing and logistics of electricity consumption information collection equipment in a smart park. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention discloses a remote monitoring system for storage and logistics of electricity consumption information collection equipment in a smart park. The present invention introduces a network security module and adopts a blockchain encryption model to ensure the confidentiality, integrity and identity authentication of data transmission. In this way, security risks such as network attacks and data leakage can be prevented, and the security of the system can be improved. By introducing an artificial intelligence module, data is analyzed and predicted based on a scheduling optimization algorithm model. Through intelligent scheduling and optimization, resource utilization and transportation efficiency can be improved, and the problem of low efficiency of traditional scheduling methods can be solved. By introducing a risk warning module, warnings are given based on historical data and real-time data and decision support is provided. In this way, potential risks can be discovered in a timely manner, and corresponding measures can be taken to solve the problem that traditional risk warning methods are prone to omissions and false alarms. By introducing a system integration and interconnection module, the integration of various modules is realized through a collaborative sharing system, and interconnected with other smart park equipment and systems. In this way, the problem of information islands between various modules in the existing system can be solved, information sharing and collaborative work can be realized, and overall management efficiency can be improved. By introducing a privacy protection module, personal and sensitive information can be protected through privacy data desensitization and permission management mechanisms. In this way, personal sensitive information can be ensured not to face the risk of privacy leakage, and the privacy security of the system can be enhanced.

[0006] In order to achieve the above technical effects, the present invention adopts the following technical solutions: A remote monitoring system for storage and logistics of electricity consumption information collection equipment in a smart park, wherein the system comprises: An intelligent sensor module, which collects equipment storage logistics environment parameters through sensors and IoT tags for electricity usage information, and sends the data to a central server module through a wireless communication network; A video monitoring module, which uses a camera and an image processing method to perform real-time video monitoring and analysis of the storage logistics area; the image processing method uses a target detection algorithm model to achieve anomaly detection and target tracking functions; A central server module, which stores, processes and analyzes sensor data and video surveillance data in real time through a big data storage and analysis system; A remote control module, which provides a remote control function through a logistics monitoring cloud platform, and the logistics monitoring cloud platform remotely monitors and controls storage logistics equipment and systems through remote communication protocols and secure access methods; A network security module, which ensures confidentiality, integrity and identity authentication of data transmission through a blockchain encryption model; An artificial intelligence module, which automatically schedules and plans warehouse logistics based on a scheduling optimization algorithm model; A risk warning module, which provides warnings and decision support through risk assessment models based on historical data and real-time data; A system integration and interconnection module, which realizes information sharing and collaborative work through a collaborative sharing system; A privacy protection module, which protects personal sensitive information in the warehousing and logistics system through privacy data desensitization and permission management mechanisms; Among them, the output ends of the intelligent sensor module and the video surveillance module are connected to the input end of the central server module; the output ends of the central server module and the privacy protection module are connected to the input end of the remote control module; the output end of the remote control module is connected to the input end of the network security module and the artificial intelligence module; the output end of the artificial intelligence module is connected to the input end of the risk warning module.

[0007] As a further technical solution of the present invention, the target detection algorithm model includes a data protocol identification module, a data conversion module, a convolutional neural network extraction module, a data area feature classification module and a loss function calculation module, wherein the data protocol identification module is used to identify the input electricity information collection equipment storage logistics information communication protocol, the data conversion module is used to convert the input data information into a data protocol recognized by the convolutional neural network, the convolutional neural network extraction module performs feature extraction on the input data information through an extraction function, the data area feature classification module is used to perform data area feature classification according to the electricity information collection equipment storage logistics, the loss function calculation module is used to select a loss calculation method according to the task type of the function model, the output end of the data protocol identification module is connected to the input end of the data conversion module, the output end of the data conversion module is connected to the input end of the convolutional neural network extraction module, the output end of the convolutional neural network extraction module is connected to the input end of the data area feature classification module, and the output end of the data area feature classification module is connected to the input end of the loss function calculation module.

[0008] As a further technical solution of the present invention, the feature extraction function of the convolutional neural network extraction module automatically learns feature representations with semantic information from the original image through a combination of a convolutional layer and a pooling layer; the formula expression of the feature extraction function based on the convolutional neural network is: (1) In formula (1), M represents the output feature map, which contains the semantic information of the original image; N represents the input image; represents the convolutional layer; represents the pooling layer; multiple candidate boxes are generated through the region proposal network function to locate the target position; the formula expression of the region proposal network function is: (2) In formula (2), Y represents multiple candidate boxes generated to locate the target position; represents the input feature map, which is used as the input of the region proposal network; b represents the weight parameter of the region proposal network; the target detection result is screened out by the non-maximum suppression method; the non-maximum suppression method determines the target bounding box by the target detection function; the data encryption unit encrypts and decrypts the data by an asymmetric encryption algorithm; the data encryption unit encrypts and decrypts the data by an asymmetric encryption algorithm; the target detection function locates the position and size of the target by the prior box and the offset; the formula expression of the target detection function is: (3) In formula (3), R represents the The first The probability score of a candidate box being a target; represents the probability that the candidate box contains the target; m is the overlap between the candidate box and the true target; S represents the offset of the candidate box relative to the prior box.

[0009] As a further technical solution of the present invention, the big data storage and analysis system includes a data receiving module, a real-time data processing module, a data storage module and a data analysis module; the data receiving module includes a sensor interface unit and a video monitoring interface unit; the sensor interface unit exchanges data with various sensors through a serial communication protocol; the video monitoring interface unit receives video monitoring data through a streaming media transmission protocol; the real-time data processing module includes a feature calculation unit and an analysis and prediction unit; the feature calculation unit performs real-time processing and calculation on the collected data through a streaming computing engine; the analysis and prediction unit analyzes and models the real-time data through a statistical analysis method; the data storage module includes a database management unit and a storage medium unit; the database management unit stores and manages data through a database management system; the storage medium unit physically stores the processed data through a hard disk storage method; the data analysis module includes an offline analysis unit and a deep analysis unit; the offline analysis unit extracts relevant information and patterns through a batch processing method; the deep analysis unit mines and models data through association rule mining and clustering analysis methods.

[0010] As a further technical solution of the present invention, the logistics monitoring cloud platform includes a remote communication module, a security access module, a visualization module and a remote control module; the remote communication module realizes real-time data transmission and instruction issuance functions with warehousing logistics equipment and systems through a remote communication protocol; the security access module includes an authentication unit and a data encryption unit; the authentication unit authenticates and authorizes the user identity through a username, password and a digital certificate; the data encryption unit encrypts the transmitted data through a symmetric encryption algorithm; the visualization module visualizes the received data through a chart library and a data visualization method; the remote control module includes an instruction issuance unit and a control logic unit; the instruction issuance unit sends control instructions to the warehousing logistics equipment and systems through remote procedure calls and message queues; the control logic unit processes and executes the received control instructions through a rule engine and a logic control protocol.

[0011] As a further technical solution of the present invention, the blockchain encryption model includes a blockchain management module, an identity authentication module, a data transmission security module and an anti-attack and anti-tampering module; the blockchain management module includes a consistency management unit and a blockchain storage unit; the consistency management unit realizes the consistency between nodes in the blockchain network through proof of work and proof of equity mechanism; the blockchain storage unit stores the blockchain data generated by each node through a distributed storage method; the identity authentication module includes a user identity authentication unit and a blockchain identity registration unit; the user identity authentication unit realizes the authentication and authorization of the user identity through a digital certificate and an encryption key pair; the blockchain identity registration unit realizes the registration and management of the user identity through a decentralized method of the blockchain; the data transmission security module includes a data encryption unit and a data verification unit; the data encryption unit encrypts and decrypts the data through an asymmetric encryption algorithm; the data verification unit signs and verifies the data through an elliptic curve signature method; the anti-attack and anti-tampering module includes a smart contract unit and an intrusion detection unit; the smart contract unit prevents malicious attacks and data tampering through a security audit method; the intrusion detection unit monitors network traffic and behavior in real time through an intrusion detection system and an intrusion prevention system.

[0012] As a further technical solution of the present invention, the scheduling optimization algorithm model predicts the logistics demand in the future time period through a demand prediction formula based on historical data; the formula expression of the demand prediction formula based on historical data is: (4) In formula (4), It indicates the predicted logistics demand in the future time period, which is used to provide reference for resource scheduling and route planning. Represents demand data within a historical time period; Indicates the deviation affected by external factors; maximizes the resource utilization of tasks and reduces logistics time through the resource scheduling optimization formula to achieve intelligent scheduling of warehousing logistics; the formula expression of the resource scheduling optimization formula is: (5) In formula (5), T represents the Resource utilization of each task; Indicates Logistics time for each task; Represents the trade-off parameter between resource utilization and logistics time; minimizes the overall transportation cost and time through the path planning and cargo allocation formula, and realizes the path planning and cargo allocation based on machine learning; the formula expression of the path planning and cargo allocation formula is: (6) In formula (6), Indicates The transportation cost of each route; Indicates The transportation time of each route; represents the trade-off parameter between transportation cost and time; v represents the number of optional paths.

[0013] As a further technical solution of the present invention, the risk assessment model includes a real-time monitoring module, a risk identification module, a level judgment module, a prediction and warning module, a decision support module and a model update and optimization module; the real-time monitoring module monitors at least the location, status and movement trajectory information of the goods in real time through a sensor network and a wireless sensing method; the risk identification module includes an anomaly detection unit and a cargo loss warning unit; the anomaly detection unit detects and identifies abnormal situations in the warehousing and logistics environment through a time series analysis method; the cargo loss warning unit identifies the risk of cargo loss through a logical judgment method; the level judgment module includes a risk quantification unit and a supply chain visualization unit; the risk quantification unit evaluates the risk size through a statistical analysis method; the supply chain visualization unit displays the risk assessment results through a chart library and a data visualization method; the prediction and warning module includes Prediction and analysis unit and early warning notification unit; based on historical data and real-time data, the prediction and analysis unit predicts at least cargo detention and supply chain delays through statistics and big data analysis methods; the early warning notification unit sends early warning information to relevant personnel through message push and email notification; the decision support module includes a risk decision unit and an emergency response unit; based on risk assessment results and early warning information, the risk decision unit provides decision suggestions through an expert system; the emergency response unit automatically triggers emergency measures through a rule engine; the model update and optimization module includes a model update unit and a performance optimization unit; the model update unit regularly trains and updates the risk assessment model through model fusion and incremental learning methods; the performance optimization unit optimizes the performance of the risk assessment model through model parameter adjustment, feature selection and model integration methods to improve the accuracy and efficiency of risk prediction.

[0014] As a further technical solution of the present invention, the collaborative sharing system includes a data integration module, an information sharing module, a task scheduling module and a performance optimization module; the data integration module includes a data acquisition unit and a data storage unit; the data acquisition unit collects data from various subsystems and devices through data interfaces and sensors; the data storage unit realizes safe storage and rapid access to data through databases and cloud storage methods; the information sharing module includes a data extraction unit and a data exchange unit; the data extraction unit extracts key information through data mining and analysis methods; the data exchange unit exchanges and shares data with other smart park equipment and systems through the Internet and data communication protocols; the task scheduling module includes a resource scheduling unit and a task allocation unit; the resource scheduling unit realizes dynamic scheduling and optimization of resources through real-time control methods; the task allocation unit monitors and allocates tasks through real-time communication and rule engines; the performance optimization module includes an operation monitoring unit and an optimization adjustment unit; the operation monitoring unit monitors the operation status and performance indicators of the system through real-time monitoring and performance evaluation methods; the optimization adjustment unit optimizes and adjusts the performance of the system according to the monitoring results through intelligent optimization algorithms and real-time control methods.

[0015] As a further technical solution of the present invention, the privacy protection module includes a privacy data collection and desensitization submodule, a permission management and access control submodule and a privacy risk monitoring submodule; the privacy data collection and desensitization module includes a data desensitization unit and an anonymization unit; the data desensitization unit desensitizes the collected personal and sensitive information through differential privacy and data encryption methods; the anonymization unit converts personal information into an anonymous identifier through a hash function and an identity replacement method; the permission management and access control submodule includes an identity authentication unit and an access control unit; the identity authentication unit ensures the legitimacy of the user identity through two-factor authentication and biometric recognition methods; the access control unit implements fine-grained control and permission management of sensitive information through a role-based access control method; the privacy risk monitoring submodule includes a privacy risk assessment unit and a privacy event monitoring unit; the privacy risk assessment unit assesses the privacy data processing flow and control measures in the system through an expert system; the privacy event monitoring unit monitors the privacy data processing in the system in real time through log recording and anomaly detection methods.

[0016] Positive beneficial effects: The present invention introduces a network security module and adopts a blockchain encryption model to ensure the confidentiality, integrity and identity authentication of data transmission. In this way, security risks such as network attacks and data leakage can be prevented, and the security of the system can be improved. By introducing an artificial intelligence module, data is analyzed and predicted based on a scheduling optimization algorithm model. Through intelligent scheduling and optimization, resource utilization and transportation efficiency can be improved, and the problem of low efficiency of traditional scheduling methods can be solved. By introducing a risk warning module, warnings are given based on historical data and real-time data and decision support is provided. In this way, potential risks can be discovered in a timely manner, and corresponding measures can be taken to solve the problem that traditional risk warning methods are prone to omissions and false alarms. By introducing a system integration and interconnection module, the integration of various modules is realized through a collaborative sharing system, and interconnected with other smart park equipment and systems. In this way, the problem of information islands between various modules in the existing system can be solved, information sharing and collaborative work can be realized, and overall management efficiency can be improved. By introducing a privacy protection module, personal and sensitive information can be protected through privacy data desensitization and permission management mechanisms. In this way, personal sensitive information can be ensured not to face the risk of privacy leakage, and the privacy security of the system can be enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which: Figure 1 This is a framework diagram of a remote monitoring system for storage and logistics of electricity consumption information collection equipment in a smart park according to the present invention; Figure 2 A working method step diagram of the target detection algorithm model of the present invention; Figure 3 This is an architecture diagram of the logistics monitoring cloud platform of the present invention; Figure 4 It is a principle framework diagram of the risk assessment model of the present invention; Figure 5 This is a schematic diagram of the process steps of a remote monitoring system for warehousing and logistics of a smart park electricity consumption information collection device according to the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1-Figure 5 As shown, a remote monitoring system for storage and logistics of power consumption information collection equipment in a smart park includes: An intelligent sensor module, which collects equipment storage logistics environment parameters through sensors and IoT tags for electricity usage information, and sends the data to a central server module through a wireless communication network; A video monitoring module, which uses a camera and an image processing method to perform real-time video monitoring and analysis of the storage logistics area; the image processing method uses a target detection algorithm model to achieve anomaly detection and target tracking functions; A central server module, which stores, processes and analyzes sensor data and video surveillance data in real time through a big data storage and analysis system; A remote control module, which provides a remote control function through a logistics monitoring cloud platform, and the logistics monitoring cloud platform remotely monitors and controls storage logistics equipment and systems through remote communication protocols and secure access methods; A network security module, which ensures confidentiality, integrity and identity authentication of data transmission through a blockchain encryption model; An artificial intelligence module, which automatically schedules and plans warehouse logistics based on a scheduling optimization algorithm model; A risk warning module, which provides warnings and decision support through risk assessment models based on historical data and real-time data; A system integration and interconnection module, which realizes information sharing and collaborative work through a collaborative sharing system; A privacy protection module, which protects personal sensitive information in the warehousing and logistics system through privacy data desensitization and permission management mechanisms; Among them, the output ends of the intelligent sensor module and the video surveillance module are connected to the input end of the central server module; the output ends of the central server module and the privacy protection module are connected to the input end of the remote control module; the output end of the remote control module is connected to the input end of the network security module and the artificial intelligence module; the output end of the artificial intelligence module is connected to the input end of the risk warning module.

[0020] The target detection algorithm model includes a data protocol identification module, a data conversion module, a convolutional neural network extraction module, a data area feature classification module and a loss function calculation module, wherein the data protocol identification module is used to identify the input power information collection equipment storage logistics information communication protocol, the data conversion module is used to convert the input data information into a data protocol recognized by the convolutional neural network, the convolutional neural network extraction module performs feature extraction on the input data information through an extraction function, the data area feature classification module is used to perform data area feature classification according to the power information collection equipment storage logistics, the loss function calculation module is used to select a loss calculation method according to the task type of the function model, the output end of the data protocol identification module is connected to the input end of the data conversion module, the output end of the data conversion module is connected to the input end of the convolutional neural network extraction module, the output end of the convolutional neural network extraction module is connected to the input end of the data area feature classification module, and the output end of the data area feature classification module is connected to the input end of the loss function calculation module.

[0021] In a further embodiment, the data protocol identification module is a key component in the field of network monitoring and network security. Its main function is to identify the type of protocol used by the data packets in transit. In the network, data is transmitted in the form of data packets, each of which contains a series of header information, including protocol identifiers, such as TCP, UDP, HTTP, HTTPS, etc. The workflow of the data protocol identification module is as follows: For example, the module first needs to be able to capture data packets in the network. This is usually achieved through the promiscuous mode of the network interface card (NIC), allowing the module to capture all data packets passing through the network interface, not just those destined for the local host. The captured data packets are sent to the protocol parsing module. This module analyzes the header information of the data packet, especially the protocol identifier, to determine the type of protocol used by the data packet. Based on the header information, the module classifies the data packet into the corresponding protocol type. This may include the identification of known protocols, as well as the analysis and identification of unknown or unidentified protocols. For unknown protocols, the module may take further actions, such as deep packet inspection (DPI), to guess or infer the nature of the protocol. This may involve a more detailed analysis of the content of the data packet to determine the type of protocol it belongs to. The identified protocol information will be recorded for logging and reporting. This information is critical for network monitoring, traffic analysis, and the development of network security strategies.

[0022] In a further embodiment, the data conversion module is responsible for converting the raw data into a format suitable for neural network processing. This may include operations such as data standardization, normalization, and reshaping the data to fit the network input requirements. This module ensures that the input data is suitable for subsequent convolutional neural network processing in terms of numerical range and dimension, improving the efficiency and accuracy of model training.

[0023] In a further embodiment, the convolutional neural network extraction module automatically learns and extracts features from the input data through a series of convolutional layers and pooling layers. This module uses convolution operations to identify local patterns, such as edges and textures in images, and pooling operations help reduce the amount of data while retaining important information. This is the core part of the deep learning model for feature extraction.

[0024] In a further embodiment, the data region feature classification module further processes the features extracted by the convolutional neural network, typically including a fully connected layer for classification or regression tasks. This module converts the feature map into a one-dimensional vector and then uses a fully connected layer to make the final classification or regression decision. It can abstract the features and make predictions.

[0025] In a further embodiment, the difference between the prediction result of the loss function calculation module and the actual label is a key indicator in the model training process. By calculating the loss function, the module provides guidance for the back propagation algorithm to determine how to adjust the weights of the network to minimize the prediction error. Common loss functions include cross entropy loss and mean square error loss.

[0026] As a further embodiment of the present invention, the feature extraction function of the convolutional neural network extraction module automatically learns feature representations with semantic information from the original image through a combination of a convolutional layer and a pooling layer; the formula expression of the feature extraction function based on the convolutional neural network is: (1) In formula (1), M represents the output feature map, which contains the semantic information of the original image; N represents the input image; represents the convolutional layer; represents the pooling layer; multiple candidate boxes are generated through the region proposal network function to locate the target position; the formula expression of the region proposal network function is: (2) In formula (2), Y represents multiple candidate boxes generated to locate the target position; represents the input feature map, which is used as the input of the region proposal network; b represents the weight parameter of the region proposal network; the target detection result is screened out by the non-maximum suppression method; the non-maximum suppression method determines the target bounding box by the target detection function; the data encryption unit encrypts and decrypts the data by an asymmetric encryption algorithm; the data encryption unit encrypts and decrypts the data by an asymmetric encryption algorithm; the target detection function locates the position and size of the target by the prior box and the offset; the formula expression of the target detection function is: (3) In formula (3), R represents the The first The probability score of a candidate box being a target; represents the probability that the candidate box contains the target; m is the overlap between the candidate box and the true target; S represents the offset of the candidate box relative to the prior box.

[0027] In a specific embodiment, the operating hardware environment of the target detection algorithm model includes: a host, a server or a cloud computing platform; a GPU, which is used to accelerate the training and reasoning process of the deep learning model; a camera, which is used to collect image data of the monitoring area; in a specific implementation, the iterative data table of the target detection algorithm model is shown in Table 1: Table 1 Iteration data table of target detection algorithm model

[0028] In Data Table 1, starting from the 6th iteration, the accuracy of the model on the training set and the test set gradually increased, and the loss function value gradually decreased. From the 8th iteration to the 9th iteration, the accuracy and loss function value changed little, indicating that the model has reached a relatively stable state. This stable state means that the performance of the model on the training set and the test set is relatively consistent, and no large fluctuations occur. It can be considered that the model has converged and achieved good target detection results.

[0029] In a remote monitoring system for warehousing and logistics of electricity consumption information collection equipment in a smart park, the working steps of the target detection algorithm model are as follows: T1. Data collection: The system uses cameras installed in the warehouse to photograph items during transportation and transmits the image data to the target detection algorithm model.

[0030] T2. Data preprocessing: In the target detection algorithm, a series of preprocessing is required on the original image, including image scaling, normalization, grayscale, enhancement and other operations, in order to perform subsequent feature extraction and target detection.

[0031] T3. Feature extraction: After preprocessing, the image is input into the convolutional neural network (CNN), and the feature information in the image is extracted through multi-layer convolution and pooling operations.

[0032] T4. Target detection: After feature extraction, target detection algorithms such as YOLO and Faster R-CNN are applied to identify and locate targets in the image, and determine whether the target is an abnormal object.

[0033] T5. Target tracking: If the target is judged to be a normal object, target tracking is performed, that is, tracking the position and movement trajectory of the same object in continuous images.

[0034] T6. Anomaly detection: If the target is judged as an abnormal item, anomaly detection will be performed, which will trigger the corresponding early warning mechanism and promptly notify relevant personnel for processing.

[0035] In the specific implementation, the machine learning algorithm can automatically identify and detect targets without human intervention, thus improving work efficiency and accuracy. In addition, the target detection algorithm based on the deep learning model can efficiently identify and locate targets in complex scenarios and realize real-time monitoring. Secondly, the machine learning algorithm can continuously optimize its own model, improve the precision and accuracy of target detection, and effectively avoid problems such as misjudgment or missed judgment. At the same time, the machine learning algorithm can flexibly adjust parameters and models according to different warehouse scenarios and item characteristics to adapt to different detection needs. In the specific implementation, the data test comparison example table of the target detection algorithm model and the traditional algorithm is shown in Table 2: Table 2 Target detection algorithm model data test comparison table

[0036] In Data Table 2, the target detection algorithm model has greatly improved detection time and accuracy compared to traditional algorithms. It can identify and locate targets more quickly and accurately, bringing positive results to the implementation of the remote monitoring system for warehousing and logistics of electricity consumption information collection equipment in smart parks.

[0037] In the above embodiment, the big data storage and analysis system includes a data receiving module, a real-time data processing module, a data storage module and a data analysis module; the data receiving module includes a sensor interface unit and a video monitoring interface unit; the sensor interface unit interacts with various sensors through a serial communication protocol; the video monitoring interface unit receives video monitoring data through a streaming media transmission protocol; the real-time data processing module includes a feature calculation unit and an analysis and prediction unit; the feature calculation unit performs real-time processing and calculation on the collected data through a streaming computing engine; the analysis and prediction unit analyzes and models the real-time data through a statistical analysis method; the data storage module includes a database management unit and a storage medium unit; the database management unit stores and manages data through a database management system; the storage medium unit physically stores the processed data through a hard disk storage method; the data analysis module includes an offline analysis unit and a deep analysis unit; the offline analysis unit extracts relevant information and patterns through a batch processing method; the deep analysis unit mines and models data through association rule mining and clustering analysis methods.

[0038] In a specific embodiment, the working principle of the big data storage and analysis system in the remote monitoring of smart park warehousing logistics is as follows: m1. Data collection and transmission: The smart park electricity consumption information collection equipment warehousing logistics remote monitoring system collects data in real time through various sensors and monitoring equipment. These data can include environmental parameters (such as temperature, humidity, gas concentration), equipment status (such as access control, cameras, mobile devices), and the location and operating status of transportation tools (such as trucks and drones). The collected data is transmitted to the data center or cloud platform through the network.

[0039] m2. Data preprocessing: Once the data arrives at the data center or cloud platform, it needs to be preprocessed. This step includes data format conversion, data cleaning, anomaly detection and verification, etc. Data format conversion unifies the data collected by different sensors or devices into a format that the system can recognize, which is convenient for subsequent processing.

[0040] m3. Data storage and management: The pre-processed data is stored in a big data storage system. These storage systems can use distributed file systems (such as Hadoop HDFS) or cloud storage services (such as Amazon S3, Google Cloud Storage).

[0041] m4. Data analysis and mining: Once the data is stored, various data analysis and mining tasks can be performed. This includes data analysis based on statistical methods and machine learning algorithms, such as clustering, classification, association rule mining, time series analysis, etc.

[0042] m5. Data visualization and report generation: Data analysis results can be presented to users in a visual way, such as charts, dashboards, etc. Data visualization makes data easier to understand and interpret, helping managers to intuitively identify trends and problems.

[0043] In the above embodiment, the logistics monitoring cloud platform includes a remote communication module, a security access module, a visualization module and a remote control module; the remote communication module realizes real-time data transmission and instruction issuance functions with warehousing logistics equipment and systems through a remote communication protocol; the security access module includes an authentication unit and a data encryption unit; the authentication unit authenticates and authorizes the user identity through a username, password and digital certificate; the data encryption unit encrypts the transmitted data through a symmetric encryption algorithm; the visualization module visualizes the received data through a chart library and a data visualization method; the remote control module includes an instruction issuance unit and a control logic unit; the instruction issuance unit sends control instructions to the warehousing logistics equipment and systems through remote procedure calls and message queues; the control logic unit processes and executes the received control instructions through a rule engine and a logic control protocol.

[0044] In a specific embodiment, the logistics monitoring cloud platform communicates remotely with each storage and logistics equipment through a remote communication module. Data transmission and command issuance are achieved through network connection. For example, Internet of Things technology (such as sensors, RFID, etc.) can be used to interact with the equipment in real time. In order to ensure the security and reliability of data, in the logistics monitoring cloud platform, the security access module is responsible for establishing an encrypted communication channel and encrypting the data for transmission. The module can also ensure that only legitimate users can access and operate the system through identity authentication and permission management mechanisms. The visualization display module displays data to users in the form of charts, dashboards, etc. Through an intuitive visualization interface, users can monitor the status, operation status and environmental parameters of storage and logistics equipment in real time. This enables managers to more easily understand the operating status of the entire storage and logistics system. The remote control module allows users to remotely control storage and logistics equipment through the cloud platform. For example, instructions can be issued through the cloud platform to turn on / off equipment, adjust parameter settings, trigger alarms, etc. This provides convenience and flexibility, allowing managers to respond and handle various situations in a timely manner.

[0045] In the above embodiment, the blockchain encryption model includes a blockchain management module, an identity authentication module, a data transmission security module and an anti-attack and anti-tampering module; the blockchain management module includes a consistency management unit and a blockchain storage unit; the consistency management unit realizes the consistency between nodes in the blockchain network through proof of work and proof of equity mechanism; the blockchain storage unit stores the blockchain data generated by each node through a distributed storage method; the identity authentication module includes a user identity authentication unit and a blockchain identity registration unit; the user identity authentication unit realizes the authentication and authorization of the user identity through a digital certificate and an encryption key pair; the blockchain identity registration unit realizes the registration and management of the user identity through a decentralized method of the blockchain; the data transmission security module includes a data encryption unit and a data verification unit; the data encryption unit encrypts and decrypts the data through an asymmetric encryption algorithm; the data verification unit signs and verifies the data through an elliptic curve signature method; the anti-attack and anti-tampering module includes a smart contract unit and an intrusion detection unit; the smart contract unit prevents malicious attacks and data tampering through a security audit method; the intrusion detection unit monitors network traffic and behavior in real time through an intrusion detection system and an intrusion prevention system.

[0046] In a specific embodiment, the blockchain encryption model uses a distributed decentralized structure to store data on multiple nodes to ensure the security and non-tamperability of the data. When new data is generated, after being processed by the encryption algorithm, a unique digital fingerprint (hash) will be generated, and consensus will be reached through the consensus algorithm, and then written into the blockchain. In this way, any tampering of the data will be immediately discovered by other nodes, thereby effectively ensuring the integrity and reliability of the data. The blockchain encryption model adopts an identity authentication mechanism based on public key encryption, and each participant has his own private key and public key. When a user conducts a transaction or operation, he needs to verify his identity through a digital signature, and determine the corresponding authority according to the smart contract. This mechanism ensures that the identity of the participant is authentic and reliable, and limits the scope of his operation authority, thereby improving the security of the system. During the data transmission process, the blockchain encryption model uses advanced encryption technologies, such as AES, RSA, etc., to encrypt the data to ensure the confidentiality and security of the data during the transmission process. At the same time, the combination of symmetric keys and asymmetric keys can also ensure the security and integrity of data transmission. In order to prevent data from being tampered with, the blockchain encryption model uses the structure of Merkle tree or other hash tree to hash the data level by level to form an unalterable data structure. In this way, even if someone tries to tamper with a data block, the entire hash chain will be destroyed and rejected by other nodes, ensuring that the data cannot be tampered with. In addition, the blockchain encryption model has set up multiple anti-attack and protection mechanisms, including intrusion detection, DDoS protection, abnormal behavior monitoring and other technical means. These mechanisms can detect and prevent malicious attacks in a timely manner, ensuring the stability and security of the system.

[0047] In the above embodiment, the scheduling optimization algorithm model predicts the logistics demand in the future time period through a demand prediction formula based on historical data; the formula expression of the demand prediction formula based on historical data is: (4) In formula (4), It indicates the predicted logistics demand in the future time period, which is used to provide reference for resource scheduling and route planning. Represents demand data within a historical time period; Indicates the deviation affected by external factors; maximizes the resource utilization of tasks and reduces logistics time through the resource scheduling optimization formula to achieve intelligent scheduling of warehousing logistics; the formula expression of the resource scheduling optimization formula is: (5) In formula (5), T represents the Resource utilization of each task; Indicates Logistics time for each task; Represents the trade-off parameter between resource utilization and logistics time; minimizes the overall transportation cost and time through the path planning and cargo allocation formula, and realizes the path planning and cargo allocation based on machine learning; the formula expression of the path planning and cargo allocation formula is: (6) In formula (6), Indicates The transportation cost of each route; Indicates The transportation time of each route; represents the trade-off parameter between transportation cost and time; v represents the number of optional paths.

[0048] In a specific embodiment, the scheduling optimization algorithm model runs in the following hardware environment: a server or cloud platform for hosting and executing the algorithm model, processing large-scale data sets and complex computing tasks. A multi-core processor for parallel computing and improving the efficiency of the algorithm. A storage device for storing a large amount of warehouse logistics data and algorithm models. A network device for data interaction with remote warehouses, sensors, and other systems.

[0049] In a remote monitoring system for warehousing and logistics of electricity consumption information collection equipment in a smart park, the operation process based on the scheduling optimization algorithm model is as follows: Q1. Data collection and preprocessing: The system collects real-time warehouse logistics data through sensors and monitoring equipment. These data include cargo information, warehouse status, traffic conditions, etc. The data is then cleaned, converted, and features extracted for subsequent analysis and prediction.

[0050] Q2. Model training: Use historical data sets to train the model. During the training process, the algorithm will learn the scheduling strategy and optimize the objective function based on the input features and labels in the data set (for example, cargo type, destination, cargo weight, etc.). Common machine learning algorithms include regression, decision trees, neural networks, etc.

[0051] Q3. Model evaluation and tuning: After training is completed, the model is evaluated using the test data set. Evaluation indicators can include accuracy, recall, F1 score, etc. Based on the evaluation results, the model can be tuned, such as adjusting hyperparameters, changing the model structure, etc.

[0052] Q4. Prediction and Scheduling: In actual operation, the system will receive new warehouse logistics data in real time and use the trained model to make predictions and scheduling decisions. Based on the current input features, the model will generate corresponding scheduling plans, such as the order of picking goods, transportation routes, etc.

[0053] Q5. Feedback and Update: The system continuously optimizes and updates the model based on actual execution results and feedback information. This can help the algorithm adapt to the dynamically changing operating environment and improve scheduling results.

[0054] In the specific implementation, the test data table based on the scheduling optimization algorithm model is shown in Table 3: Table 3 Test data based on scheduling optimization algorithm model

[0055] In data table 3, the starting position is the starting position of the goods, indicating the specific area or warehouse location where the goods are located, such as A1, B3, C2, etc. The weight of the goods (kg) indicates the weight of the goods in kilograms. The volume of the goods (m³) indicates the volume of the goods in cubic meters. The expected picking time (minutes) indicates the time required for picking the goods predicted by the model, in minutes. The actual picking time (minutes) indicates the time required for picking the goods recorded during the actual picking process, in minutes. Temperature (Celsius) indicates the temperature of the warehouse environment, in degrees Celsius. Humidity (%) indicates the humidity of the warehouse environment, expressed as a percentage. Traffic conditions indicate the traffic conditions on the cargo transportation route, such as unobstructed, congested, etc. Warehouse status indicates the working status of the warehouse, such as normal, abnormal, etc. In the specific implementation, the data test comparison example table based on the scheduling optimization algorithm model and the traditional algorithm is shown in Table 4: Table 4 Comparison data based on scheduling optimization algorithm model

[0056] In the above table, "cargo loss rate" refers to the probability of cargo loss during transportation; "average waiting time" refers to the average time cargo waits for loading and unloading and transportation. As can be seen from the table, machine learning algorithms show better warehouse logistics optimization effects than traditional algorithms in all test data sets. The total transportation time of machine learning algorithms is shorter, and the transportation cost is lower, and the cargo loss rate is also smaller. In addition, by comparing the average waiting time, it can be seen that machine learning algorithms can better dispatch and arrange cargo, reduce the waiting time of cargo, and improve the overall service quality and customer satisfaction.

[0057] In the above embodiment, the risk assessment model includes a real-time monitoring module, a risk identification module, a level judgment module, a prediction and warning module, a decision support module and a model update and optimization module; the real-time monitoring module monitors at least the location, status and movement trajectory information of the goods in real time through a sensor network and a wireless sensing method; the risk identification module includes an anomaly detection unit and a cargo loss warning unit; the anomaly detection unit detects and identifies abnormal situations in the warehousing and logistics environment through a time series analysis method; the cargo loss warning unit identifies the risk of cargo loss through a logical judgment method; the level judgment module includes a risk quantification unit and a supply chain visualization unit; the risk quantification unit evaluates the risk size through a statistical analysis method; the supply chain visualization unit displays the risk assessment results through a chart library and a data visualization method; the prediction and warning module includes a prediction analysis ... analysis unit and early warning notification unit; based on historical data and real-time data, the prediction and analysis unit predicts at least cargo detention and supply chain delays through statistical and big data analysis methods; the early warning notification unit sends early warning information to relevant personnel through message push and email notification; the decision support module includes a risk decision unit and an emergency response unit; based on risk assessment results and early warning information, the risk decision unit provides decision suggestions through an expert system; the emergency response unit automatically triggers emergency measures through a rule engine; the model update and optimization module includes a model update unit and a performance optimization unit; the model update unit regularly trains and updates the risk assessment model through model fusion and incremental learning methods; the performance optimization unit optimizes the performance of the risk assessment model through model parameter adjustment, feature selection and model integration methods to improve the accuracy and efficiency of risk prediction.

[0058] In a specific embodiment, the risk assessment model collects data in the warehousing and logistics environment in real time through the real-time monitoring module, including temperature, humidity, gas concentration, equipment operation status, personnel activities, etc. These data are sent to the data processing unit, and after filtering, data cleaning, feature extraction and other steps, structured data is formed to provide a basis for subsequent risk identification and assessment. The risk identification module uses data mining, machine learning and other technologies to analyze and process the data transmitted by the real-time monitoring module to identify potential risk factors, such as equipment abnormalities, operation abnormalities, personnel gathering, safety hazards, etc. At the same time, the module will compare with historical data to find regular abnormal situations in order to better predict possible risks in the future. On the basis of risk identification, a risk assessment model is established using professional knowledge and experience to conduct a comprehensive assessment of the identified risks. This may involve quantitative analysis (such as probability statistics) and qualitative analysis (expert judgment) to determine the severity and possibility of the risk, thereby providing a basis for subsequent prediction and early warning. Combined with the risk assessment results and historical data, time series analysis, machine learning and other methods are used to predict possible risk events in the future and set corresponding thresholds. Once the warning threshold is reached or exceeded, a warning signal is triggered to notify relevant personnel to intervene and handle. The decision support module reports risk assessment results and early warning information to managers and relevant personnel to assist them in developing reasonable response plans and decisions. Based on the risk events predicted by the model, managers can allocate resources and develop emergency plans in a timely manner to reduce the losses caused by risks. At the same time, big data analysis technology can be used to mine hidden information in historical data for model optimization and decision support. The risk assessment model is regularly updated and optimized through the model update and optimization module, and new data, algorithms or model structures are introduced to adapt to the ever-changing warehousing and logistics environment and improve the accuracy and robustness of the model.

[0059] In the above embodiment, the collaborative sharing system includes a data integration module, an information sharing module, a task scheduling module and a performance optimization module; the data integration module includes a data acquisition unit and a data storage unit; the data acquisition unit collects data from various subsystems and devices through data interfaces and sensors; the data storage unit realizes safe storage and rapid access to data through databases and cloud storage methods; the information sharing module includes a data extraction unit and a data exchange unit; the data extraction unit extracts key information through data mining and analysis methods; the data exchange unit exchanges and shares data with other smart park equipment and systems through the Internet and data communication protocols; the task scheduling module includes a resource scheduling unit and a task allocation unit; the resource scheduling unit realizes dynamic scheduling and optimization of resources through real-time control methods; the task allocation unit monitors and allocates tasks through real-time communication and rule engines; the performance optimization module includes an operation monitoring unit and an optimization adjustment unit; the operation monitoring unit monitors the operation status and performance indicators of the system through real-time monitoring and performance evaluation methods; the optimization adjustment unit optimizes and adjusts the system performance according to the monitoring results through intelligent optimization algorithms and real-time control methods.

[0060] In a specific embodiment, the collaborative sharing system collects data from each subsystem and device through a data integration module, and integrates and summarizes it to form a unified data set. The data may include sensor data, device status, task completion status, etc. The data integration module interacts with other modules through standardized interfaces and protocols. The data integrated by the data integration module is transmitted and shared through the information sharing module. The module pushes the data to the required modules or user terminals to achieve information sharing and transmission. Different modules and users can subscribe to relevant data information as needed to obtain and share the latest data in real time. The task scheduling module is used to reasonably allocate and schedule tasks according to various task requirements and resource conditions in the system through scheduling algorithms and optimization strategies. The module will dynamically assign tasks to each subsystem based on real-time data and requirements to ensure that the tasks can be completed efficiently and meet various performance indicators. The performance optimization module analyzes the data and monitoring indicators in the system to evaluate and optimize the system. The module can detect performance bottlenecks and problems in the system, and put forward corresponding optimization suggestions and strategies to improve the overall performance, efficiency and stability of the system.

[0061] In the above embodiment, the privacy protection module includes a privacy data collection and desensitization submodule, a permission management and access control submodule and a privacy risk monitoring submodule; the privacy data collection and desensitization module includes a data desensitization unit and an anonymization unit; the data desensitization unit desensitizes the collected personal and sensitive information through differential privacy and data encryption methods; the anonymization unit converts personal information into an anonymous identifier through a hash function and an identity replacement method; the permission management and access control submodule includes an identity authentication unit and an access control unit; the identity authentication unit ensures the legitimacy of the user identity through two-factor authentication and biometric recognition methods; the access control unit implements fine-grained control and permission management of sensitive information through a role-based access control method; the privacy risk monitoring submodule includes a privacy risk assessment unit and a privacy event monitoring unit; the privacy risk assessment unit assesses the privacy data processing process and control measures in the system through an expert system; the privacy event monitoring unit monitors the privacy data processing in the system in real time through log recording and anomaly detection methods.

[0062] In a specific embodiment, the privacy protection module collects the privacy data in the system through the privacy data collection and desensitization submodule, and performs desensitization on it. When collecting data, the data fields containing sensitive information will be identified and marked according to the privacy protection rules and relevant laws and regulations. Then, these sensitive data are desensitized, such as deleted, replaced or encrypted, to protect the privacy of the user. The authority management and access control submodule is used to manage the user's authority and access control to the data. The system administrator can set the authority level and data access rights of different users or user groups. Through the identity authentication and authorization mechanism, only authorized users can access specific data and functions to ensure the security and privacy protection of the data. The privacy risk monitoring submodule monitors the privacy risk situation in the system, and promptly discovers and responds to potential privacy and security issues. Through the monitoring and analysis of data access, use and sharing, abnormal behavior or risk events can be identified, and corresponding early warnings and security measures can be triggered to protect the privacy and data security of users.

[0063] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A remote monitoring system for storage and logistics of electricity information collection equipment in a smart park, characterized by: The system comprises: An intelligent sensor module, which collects equipment storage logistics environment parameters through sensors and IoT tags for electricity usage information, and sends the data to a central server module through a wireless communication network; A video monitoring module, which uses a camera and an image processing method to perform real-time video monitoring and analysis of the storage logistics area; the image processing method uses a target detection algorithm model to achieve anomaly detection and target tracking functions; A central server module, which stores, processes and analyzes sensor data and video surveillance data in real time through a big data storage and analysis system; A remote control module, which provides a remote control function through a logistics monitoring cloud platform, and the logistics monitoring cloud platform remotely monitors and controls storage logistics equipment and systems through remote communication protocols and secure access methods; A network security module, which ensures confidentiality, integrity and identity authentication of data transmission through a blockchain encryption model; An artificial intelligence module, which automatically schedules and plans warehouse logistics based on a scheduling optimization algorithm model; A risk warning module, which provides warnings and decision support through risk assessment models based on historical data and real-time data; A system integration and interconnection module, which realizes information sharing and collaborative work through a collaborative sharing system; A privacy protection module, which protects personal sensitive information in the warehousing and logistics system through privacy data desensitization and permission management mechanisms; Among them, the output ends of the intelligent sensor module and the video surveillance module are connected to the input end of the central server module; the output ends of the central server module, the system integration and interconnection module and the privacy protection module are connected to the input end of the remote control module; the output end of the remote control module is connected to the input end of the network security module and the artificial intelligence module; the output end of the artificial intelligence module is connected to the input end of the risk warning module.

2. According to claim 1, a remote monitoring system for storage and logistics of power consumption information collection equipment in a smart park is characterized by: The target detection algorithm model includes a data protocol identification module, a data conversion module, a convolutional neural network extraction module, a data area feature classification module and a loss function calculation module, wherein the data protocol identification module is used to identify the input power consumption information collection equipment storage logistics information communication protocol, the data conversion module is used to convert the input data information into a data protocol recognized by the convolutional neural network, and the convolutional neural network extraction module extracts features from the input data information through an extraction function. The data area feature classification module is used to perform data area feature classification according to the storage logistics of the electrical information acquisition equipment, the loss function calculation module is used to select the loss calculation method according to the task type of the function model, the output end of the data protocol identification module is connected to the input end of the data conversion module, the output end of the data conversion module is connected to the input end of the convolutional neural network extraction module, the output end of the convolutional neural network extraction module is connected to the input end of the data area feature classification module, and the output end of the data area feature classification module is connected to the input end of the loss function calculation module.

3. According to claim 1, a remote monitoring system for storage and logistics of electricity consumption information collection equipment in a smart park is characterized by: The feature extraction function of the convolutional neural network extraction module automatically learns feature representations with semantic information from the original image through a combination of a convolutional layer and a pooling layer; the formula expression of the feature extraction function based on the convolutional neural network is: （1） In formula (1), M represents the output feature map, which contains the semantic information of the original image; N represents the input image; represents the convolutional layer; represents the pooling layer; multiple candidate boxes are generated through the region proposal network function to locate the target position; the formula expression of the region proposal network function is: （2） In formula (2), Y represents multiple candidate boxes generated to locate the target position; represents the input feature map, which is used as the input of the region proposal network; b represents the weight parameter of the region proposal network; the target detection result is screened out by the non-maximum suppression method; the non-maximum suppression method determines the target bounding box by the target detection function; the data encryption unit encrypts and decrypts the data by an asymmetric encryption algorithm; the data encryption unit encrypts and decrypts the data by an asymmetric encryption algorithm; the target detection function locates the position and size of the target by the prior box and the offset; the formula expression of the target detection function is: （3） In formula (3), R represents the The first The probability score of a candidate box being a target; represents the probability that the candidate box contains the target; m is the overlap between the candidate box and the true target; S represents the offset of the candidate box relative to the prior box.

4. According to claim 1, a remote monitoring system for storage and logistics of electricity consumption information collection equipment in a smart park is characterized by: The big data storage and analysis system includes a data receiving module, a real-time data processing module, a data storage module and a data analysis module; the data receiving module includes a sensor interface unit and a video monitoring interface unit; the sensor interface unit interacts with various sensors through a serial communication protocol; The video monitoring interface unit receives video monitoring data through a streaming media transmission protocol; the real-time data processing module includes a feature calculation unit and an analysis and prediction unit; The feature calculation unit processes and calculates the collected data in real time through a streaming computing engine; the analysis and prediction unit analyzes and models the real-time data through a statistical analysis method; the data storage module includes a database management unit and a storage medium unit; the database management unit stores and manages the data through a database management system; the storage medium unit physically stores the processed data through a hard disk storage method; the data analysis module includes an offline analysis unit and a deep analysis unit; the offline analysis unit extracts relevant information and patterns through a batch processing method; the deep analysis unit mines and models the data through association rule mining and clustering analysis methods.

5. According to claim 1, a remote monitoring system for storage and logistics of electricity consumption information collection equipment in a smart park, characterized in that: The logistics monitoring cloud platform includes a remote communication module, a security access module, a visualization module and a remote control module; the remote communication module realizes real-time data transmission and instruction issuing functions with warehousing logistics equipment and systems through a remote communication protocol; the security access module includes an authentication unit and a data encryption unit; the authentication unit authenticates and authorizes the user identity through a user name, password and digital certificate; the data encryption unit encrypts the transmitted data through a symmetric encryption algorithm; the visualization module visualizes the received data through a chart library and a data visualization method; the remote control module includes an instruction issuing unit and a control logic unit; The instruction issuing unit sends control instructions to the warehousing logistics equipment and system through remote procedure calls and message queues; The control logic unit processes and executes the received control instructions through a rule engine and a logic control protocol.

6. According to claim 1, a remote monitoring system for storage and logistics of electricity consumption information collection equipment in a smart park is characterized by: The blockchain encryption model includes a blockchain management module, an identity authentication module, a data transmission security module and an anti-attack and anti-tampering module; the blockchain management module includes a consistency management unit and a blockchain storage unit; the consistency management unit achieves consistency between nodes in the blockchain network through proof of work and proof of equity mechanism; the blockchain storage unit stores the blockchain data generated by each node through a distributed storage method; the identity authentication module includes a user identity authentication unit and a blockchain identity registration unit; The user identity authentication unit realizes authentication and authorization of user identity through digital certificates and encryption key pairs; the blockchain identity registration unit realizes registration and management of user identity through the decentralized method of blockchain; the data transmission security module includes a data encryption unit and a data verification unit; the data encryption unit encrypts and decrypts data through an asymmetric encryption algorithm; the data verification unit signs and verifies data through an elliptic curve signature method; the anti-attack and anti-tampering module includes a smart contract unit and an intrusion detection unit; the smart contract unit prevents malicious attacks and data tampering through a security audit method; the intrusion detection unit monitors network traffic and behavior in real time through an intrusion detection system and an intrusion prevention system.

7. According to claim 1, a remote monitoring system for storage and logistics of electricity consumption information collection equipment in a smart park, characterized in that: The scheduling optimization algorithm model predicts the logistics demand in the future time period through a demand prediction formula based on historical data; the formula expression of the demand prediction formula based on historical data is: （4） In formula (4), It indicates the predicted logistics demand in the future time period, which is used to provide reference for resource scheduling and route planning. Represents demand data within a historical time period; Indicates the deviation affected by external factors; maximizes the resource utilization of tasks and reduces logistics time through the resource scheduling optimization formula to achieve intelligent scheduling of warehousing logistics; the formula expression of the resource scheduling optimization formula is: （5） In formula (5), T represents the Resource utilization of each task; Indicates Logistics time for each task; Represents the trade-off parameter between resource utilization and logistics time; minimizes the overall transportation cost and time through the path planning and cargo allocation formula, and realizes the path planning and cargo allocation based on machine learning; the formula expression of the path planning and cargo allocation formula is: （6） In formula (6), Indicates The transportation cost of each route; Indicates The transportation time of each route; represents the trade-off parameter between transportation cost and time; v represents the number of optional paths.

8. According to claim 1, a remote monitoring system for storage and logistics of power consumption information collection equipment in a smart park, characterized in that: The risk assessment model includes a real-time monitoring module, a risk identification module, a level judgment module, a prediction and warning module, a decision support module and a model update and optimization module; the real-time monitoring module monitors at least the location, status and movement trajectory information of the goods in real time through a sensor network and a wireless sensing method; the risk identification module includes an anomaly detection unit and a cargo loss warning unit; the anomaly detection unit detects and identifies abnormal situations in the warehousing and logistics environment through a time series analysis method; The cargo loss warning unit identifies the risk of cargo loss through a logical judgment method; the level judgment module includes a risk quantification unit and a supply chain visualization unit; the risk quantification unit evaluates the risk size through a statistical analysis method; The supply chain visualization unit displays the risk assessment results through a chart library and data visualization methods; the prediction and warning module includes a prediction analysis unit and a warning notification unit; based on historical data and real-time data, the prediction analysis unit predicts at least cargo detention and supply chain delays through statistics and big data analysis methods; the warning notification unit sends warning information to relevant personnel through message push and email notification; the decision support module includes a risk decision unit and an emergency response unit; based on the risk assessment results and warning information, the risk decision unit provides decision suggestions through an expert system; The emergency response unit automatically triggers emergency measures through a rule engine; the model update and optimization module includes a model update unit and a performance optimization unit; the model update unit regularly trains and updates the risk assessment model through model fusion and incremental learning methods; the performance optimization unit optimizes the performance of the risk assessment model through model parameter adjustment, feature selection and model integration methods to improve the accuracy and efficiency of risk prediction.

9. According to claim 1, a remote monitoring system for storage and logistics of power consumption information collection equipment in a smart park, characterized in that: The collaborative sharing system includes a data integration module, an information sharing module, a task scheduling module and a performance optimization module; the data integration module includes a data acquisition unit and a data storage unit; the data acquisition unit collects data from various subsystems and devices through data interfaces and sensors; the data storage unit implements secure storage and rapid access to data through databases and cloud storage methods; the information sharing module includes a data extraction unit and a data exchange unit; the data extraction unit extracts key information through data mining and analysis methods; the data exchange unit exchanges and shares data with other smart park devices and systems through the Internet and data communication protocols; the task scheduling module includes a resource scheduling unit and a task allocation unit; the resource scheduling unit implements dynamic scheduling and optimization of resources through real-time control methods; the task allocation unit monitors and allocates tasks through real-time communication and rule engines; The performance optimization module includes an operation monitoring unit and an optimization adjustment unit; The operation monitoring unit monitors the operation status and performance indicators of the system through real-time monitoring and performance evaluation methods; The optimization and adjustment unit optimizes and adjusts the performance of the system according to the monitoring results through an intelligent optimization algorithm and a real-time control method.

10. The remote monitoring system for storage and logistics of power consumption information collection equipment in a smart park according to claim 1 is characterized by: The privacy protection module includes a privacy data collection and desensitization submodule, a permission management and access control submodule, and a privacy risk monitoring submodule; the privacy data collection and desensitization module includes a data desensitization unit and an anonymization unit; the data desensitization unit desensitizes the collected personal and sensitive information through differential privacy and data encryption methods; The anonymization unit converts personal information into an anonymous identifier through a hash function and an identity replacement method; the authority management and access control submodule includes an identity authentication unit and an access control unit; the identity authentication unit ensures the legitimacy of the user's identity through two-factor authentication and biometric recognition methods; the access control unit implements fine-grained control and authority management of sensitive information through a role-based access control method; the privacy risk monitoring submodule includes a privacy risk assessment unit and a privacy event monitoring unit; the privacy risk assessment unit assesses the privacy data processing flow and control measures in the system through an expert system; the privacy event monitoring unit monitors the privacy data processing in the system in real time through log recording and anomaly detection methods.

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