A charging pile control system based on the Internet of Things

Abstract

The present invention relates to the technical field of the Internet of Things, and discloses a charging pile control system based on the Internet of Things. The system includes: generating a charging pile hardware identifier based on charging pile identification data, digitally binding the charging pile hardware identifier to obtain a unique charging pile identifier; using the unique charging pile identifier to perform data identification on charging pile operation data to obtain fused data, and constructing a resource sharing network based on the fused data; extracting idle resources and resource demand data in the resource sharing network, constructing a multi-layer dynamic resource pool table based on the idle resource data, performing resource matching on the resource demand data and the dynamic resource pool table to obtain resource nodes; distributing data to the resource nodes based on the resource sharing network to obtain distribution data, and using the resource nodes to perform charging pile resource calculation on the distribution data to obtain allocated power. The present invention can solve the problem of insufficient control accuracy in charging pile resource allocation in current charging pile control systems.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a charging pile control system based on the Internet of Things. Background Art

[0002] With the promotion of environmental protection policies, technological advancements, and increased consumer awareness of environmental protection, the number of electric vehicles in the world has exploded in recent years. The popularity of electric vehicles has further led to a sharp increase in charging demand.

[0003] Currently, many charging piles still utilize traditional management methods. However, these systems rely on centralized authentication and management, which means that control and data storage for the entire system are centralized at a central node. If this central node is attacked, it could lead to widespread service disruptions or data tampering (for example, misreporting charging levels), compromising system security and trust. Furthermore, existing charging pile power allocation systems often employ static strategies, where the power allocation for each charging pile is preset and cannot be dynamically adjusted. With the increasing popularity of electric vehicles and charging demand, static strategies are unable to flexibly respond to demand fluctuations over time. This can lead to overloaded charging piles, idle resources in some charging piles, or the inability of some charging piles to meet charging needs during periods of high demand, reducing resource utilization efficiency. In summary, current charging pile control systems still suffer from low resource allocation efficiency and insufficient data security. Summary of the Invention

[0004] The present invention provides a charging pile control system based on the Internet of Things, the main purpose of which is to solve the problems of low resource allocation efficiency and insufficient data security in the current charging pile control system.

[0005] To achieve the above objectives, the present invention provides a charging pile control system based on the Internet of Things, comprising:

[0006] Charging pile authentication module, resource network construction module, edge computing power sharing module, charging power distribution module, specifically:

[0007] The charging pile authentication module is used to obtain charging pile data, split the charging pile data into charging pile operation data and charging pile identification data, generate the charging pile hardware identification based on the charging pile identification data, and digitally bind the charging pile hardware identification based on the asymmetric encrypted distributed ledger to obtain the unique identification of the charging pile;

[0008] The resource network construction module uses the unique identification of the charging pile to identify the charging pile operation data, obtains fused data, and builds a resource sharing network based on the Internet of Things technology and fused data;

[0009] The edge computing power sharing module extracts idle resources from the resource sharing network, obtains idle resource data, analyzes resource demand on the resource sharing network, obtains resource demand data, builds a multi-layer dynamic resource pool table based on the idle resource data, and performs resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain resource nodes.

[0010] The charging power allocation module is used to distribute data to resource nodes based on the resource sharing network and resource demand data to obtain distribution data, use the resource nodes to calculate the charging pile power based on the distribution data to obtain the allocated power, and allocate power to the charging piles based on the allocated power to obtain the charging pile power allocation result.

[0011] Optionally, when executing the function of generating a charging pile hardware identification according to the charging pile identification data, the charging pile authentication module is specifically configured to:

[0012] Normalizing the charging pile identification data to obtain normalized data;

[0013] Perform hierarchical hash calculation on the normalized data to obtain hierarchical data;

[0014] Perform XOR fusion on the layered data to obtain the charging pile hardware identification.

[0015] Optionally, when the charging pile authentication module performs the function of digitally binding the charging pile hardware identifier based on the asymmetric encryption distributed ledger to obtain the unique identifier of the charging pile, it is specifically used to:

[0016] Use the private key in the preset charging pile key pair to digitally sign the charging pile hardware identifier to obtain signature data;

[0017] Construct a tangle transaction based on the signature data and the public key in the charging station key pair;

[0018] Broadcast the tangle transaction to all nodes of the preset distributed ledger network to obtain the updated ledger;

[0019] Verify the legitimacy of the charging pile hardware identifier using the public key in the charging pile key pair, the updated ledger, and the tangle transaction to obtain a verification result;

[0020] According to the verification results, legal transactions are filtered out from the tangle transactions, the hash value of the legal transactions is calculated, and the tangle hash is obtained. The tangle hash is used as the unique identifier of the charging pile.

[0021] Optionally, when executing the function of using the unique identifier of the charging pile to identify the charging pile operation data to obtain fused data, the resource network construction module is specifically configured to:

[0022] Performing spatiotemporal data arrangement on the charging pile operation data to obtain arrangement data;

[0023] Perform demand mapping on the arranged data to obtain dynamic demand parameter data;

[0024] Calculate the hash value of the dynamic demand parameter data to obtain the running hash;

[0025] Use the private key in the charging pile key pair to sign the running hash data to obtain the running signature data;

[0026] The unique identification data of the charging pile is embedded in the signature data to obtain fused data.

[0027] Optionally, when executing the function of constructing a resource sharing network based on Internet of Things technology and fused data, the resource network construction module is specifically configured to:

[0028] Abstract the fused data into virtual resource units;

[0029] Use the preset Internet of Things communication protocol to connect the virtual resource units to obtain the connected data;

[0030] Perform resource health monitoring on Unicom data to obtain resource health status;

[0031] Dynamically calculate the weight of Unicom data based on resource health and fusion data to obtain weight data;

[0032] The network topology is constructed based on the weight data and the connection data to obtain a resource sharing network.

[0033] Optionally, when executing the function of constructing a multi-layer dynamic resource pool table according to the idle resource data, the edge computing power sharing module is specifically configured to:

[0034] Establish an initial resource pool based on idle resources;

[0035] Perform multi-layer division on the idle resources in the initial resource pool to obtain division levels;

[0036] A tree diagram is constructed based on the division levels to obtain a multi-layer resource pool table;

[0037] Regularly update idle resources according to a preset time period to obtain updated resources;

[0038] The multi-layer resource pool table is updated with updated resources to obtain a multi-layer dynamic resource pool table.

[0039] Optionally, when performing resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain the function of the resource node, the edge computing power sharing module is specifically used to:

[0040] Generate matching weights based on resource demand data;

[0041] Screen the multi-layer dynamic resource pool table according to its division levels and resource demand data to obtain candidate sub-pools;

[0042] Use the matching weight and candidate subpool to perform weighted calculation to obtain a weighted score;

[0043] Resource nodes are selected from the candidate sub-pool based on weight scores.

[0044] Optionally, when the charging power allocation module performs the function of distributing data to resource nodes according to the resource sharing network and resource demand data to obtain distribution data, it is specifically configured to:

[0045] Generate a distribution path priority list based on the resource sharing network;

[0046] Encapsulate the fused data corresponding to the resource demand data to obtain a standardized data packet;

[0047] Based on the distribution path priority list, the standardized data packet is pushed to the resource node to obtain the distribution data.

[0048] Optionally, when executing the function of calculating the charging pile power based on the distributed data using the resource node to obtain the allocated power, the charging power allocation module is specifically configured to:

[0049] Decrypt the digital signature in the distribution data using the public key corresponding to the resource node in the distribution data to obtain the decryption result;

[0050] Perform data verification on the decryption result, and perform data parsing on the distribution data that passes the data verification to obtain parsed data;

[0051] The distributed power is calculated based on the preset grid dynamic attenuation coefficient and analytical data.

[0052] Optionally, when the charging power allocation module performs the function of allocating power to the charging pile according to the allocated power and obtaining the charging pile power allocation result, it is specifically configured to:

[0053] Encapsulate the allocated power into instructions to obtain control instructions;

[0054] The control instruction is uploaded to the charging pile node corresponding to the resource demand data, and the charging pile node parses the control instruction to obtain the parsed instruction;

[0055] According to the parsing instructions, the power of the charging pile node corresponding to the resource demand data is adjusted to obtain the charging pile power allocation result.

[0056] The present invention generates a hardware identifier for charging pile data and digitally binds the identifier to the hardware identifier using an asymmetric encrypted distributed ledger, avoiding single points of failure and supporting cross-regional charging pile identity trust. Furthermore, by calculating a dynamic attenuation coefficient based on the real-time load of the power grid and allocating power based on the dynamic attenuation coefficient, it can quickly respond to changes in demand over different time periods. Therefore, the IoT-based charging pile control system proposed by the present invention can effectively solve the problem of low resource allocation efficiency that currently exists in charging pile control systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a functional module diagram of a charging pile control system based on the Internet of Things provided by one embodiment of the present invention;

[0058] Figure 2 A schematic diagram of a process for constructing a multi-layer dynamic resource pool table according to an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a resource matching process according to an embodiment of the present invention;

[0060] Figure 4 A flowchart of a charging pile control method based on the Internet of Things is provided in accordance with an embodiment of the present invention.

[0061] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] Reference Figure 1 As shown, it is a functional module diagram of a charging pile control system based on the Internet of Things provided by one embodiment of the present invention. In this embodiment, the charging pile control system 100 based on the Internet of Things can be installed in an electronic device. According to the functions implemented, the charging pile control system 100 based on the Internet of Things may include a charging pile authentication module 101, a resource network construction module 102, an edge computing power sharing module 103, and a charging power distribution module 104. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can perform fixed functions, which are stored in the memory of the electronic device.

[0064] In the embodiment of the present invention, the charging pile authentication module 101 includes obtaining charging pile data, splitting the charging pile data into charging pile operation data and charging pile identification data, generating a charging pile hardware identification based on the charging pile identification data, and digitally binding the charging pile hardware identification based on an asymmetric encrypted distributed ledger to obtain a unique identification of the charging pile;

[0065] In the embodiment of the present invention, the resource network construction module 102 includes using the unique identification of the charging pile to identify the charging pile operation data, obtain fused data, and construct a resource sharing network based on the Internet of Things technology and the fused data;

[0066] In an embodiment of the present invention, the edge computing power sharing module 103 includes extracting idle resources in the resource sharing network to obtain idle resource data, performing resource demand analysis on the resource sharing network to obtain resource demand data, constructing a multi-layer dynamic resource pool table based on the idle resource data, performing resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain resource nodes;

[0067] In an embodiment of the present invention, the charging power allocation module 104 includes distributing data to resource nodes according to a resource sharing network to obtain distributed data, calculating the charging pile power using the resource nodes to obtain allocated power, and allocating power to the charging pile according to the allocated power to obtain a charging pile power allocation result.

[0068] In detail, the modules described in the charging pile control system 100 based on the Internet of Things described in the embodiment of the present invention adopt the same technical means as the charging pile control system based on the Internet of Things described in the accompanying drawings when in use, and can produce the same technical effects, which will not be repeated here.

[0069] The following describes the various components and specific workflows of the IoT-based charging pile control system in conjunction with specific embodiments.

[0070] The charging pile authentication module 101 is used to obtain charging pile data, split the charging pile data into charging pile operation data and charging pile identification data, generate a charging pile hardware identification based on the charging pile identification data, and digitally bind the charging pile hardware identification based on an asymmetric encrypted distributed ledger to obtain a unique charging pile identification.

[0071] In the embodiment of the present invention, by acquiring charging pile data and splitting it into charging pile operation data and charging pile identification data, the unique identity of each charging pile can be accurately identified.

[0072] In the embodiment of the present invention, charging pile data refers to various information related to the charging pile, including but not limited to the operating status, operating parameters, device identification, and other performance data of the charging pile.

[0073] In an embodiment of the present invention, the charging pile operation data is mainly related to the performance and working status of the charging pile, and specifically includes real-time monitored charging pile operation data, power, charging time and other data; the charging pile identification data includes key information for identifying the identity of the charging pile, such as hardware identification, device ID and location information.

[0074] In an embodiment of the present invention, when the charging pile authentication module performs the function of generating a charging pile hardware identification according to the charging pile identification data, it is specifically used to:

[0075] Normalizing the charging pile identification data to obtain normalized data;

[0076] Perform hierarchical hash calculation on the normalized data to obtain hierarchical data;

[0077] Perform XOR fusion on the layered data to obtain the charging pile hardware identification.

[0078] In an embodiment of the present invention, the normalization process involves performing binary conversion on the identification ID in the charging pile identification data to obtain a binary string, and performing sliding window filtering on the location information data in the charging pile identification data. The window size is equal to 50 samples, and outliers outside the three standard value ranges are removed from the sampling results to obtain filtered data. The filtered data and the binary string are stored in the form of a data set to obtain normalized data.

[0079] In an embodiment of the present invention, the hierarchical hash refers to performing hash value calculation on the binary string in the normalized data to obtain hash data, performing principal component analysis on the filtered data in the normalized data to obtain reduced dimensionality data, and adding the reduced dimensionality data and the hash data to an empty data set to obtain hierarchical data.

[0080] In an embodiment of the present invention, the dimension reduction data in the hierarchical data is XOR-ed and added to the hash data to obtain XOR data, the collection time and geographical location of the charging pile identification data are obtained, an OR operation is performed based on the collection time, the geographical location, and the XOR data to obtain an operation result, and the operation result is hashed to obtain the charging pile hardware identification.

[0081] In the embodiment of the present invention, by performing hierarchical encryption and nonlinear fusion on the charging pile identification data to generate a unique identification, any tampering of a single data will cause an abnormal fusion result, thereby enhancing the overall credibility.

[0082] In an embodiment of the present invention, when the charging pile authentication module performs the function of digitally binding the charging pile hardware identifier based on the asymmetric encryption distributed ledger to obtain the unique identifier of the charging pile, it is specifically used to:

[0083] Use the private key of the preset charging pile key pair to digitally sign the charging pile hardware identifier to obtain signature data;

[0084] Construct a tangle transaction based on the signature data and the public key in the charging station key pair;

[0085] Use the public key in the charging pile key pair and the tangle transaction to verify the legitimacy of the charging pile hardware identifier and obtain the verification result;

[0086] Broadcast the tangle transaction to all nodes of the preset distributed ledger network to obtain the updated ledger;

[0087] Use the public key in the charging pile key pair, the updated ledger, and the tangle transaction to verify the legitimacy of the charging pile hardware identifier and obtain the verification result;

[0088] According to the verification results, legal transactions are filtered out from the tangle transactions, the hash value of the legal transactions is calculated, and the tangle hash is obtained. The tangle hash is used as the unique identifier of the charging pile.

[0089] In an embodiment of the present invention, the preset charging pile key pair refers to an SM2 elliptic curve key pair generated locally by the charging pile, including a private key and a public key. The initial values ​​of the private key and the public key are preset at the factory and are updated through a preset key derivation function every preset time period.

[0090] In the embodiment of the present invention, characteristic data of the charging pile is generated according to the hardware identification of the charging pile, and the characteristic data of the charging pile is signed using a private key and a signature algorithm to obtain signature data.

[0091] In an embodiment of the present invention, a tangle transaction is constructed according to the signature data, the charging pile, the location node to which the charging pile belongs, and the public key in the charging pile key pair.

[0092] In this embodiment of the present invention, after the charging station completes the signature of the hardware identifier using the private key and constructs a tangle transaction containing the signature data, public key, and other metadata (timestamp, transaction type, etc.), the tangle transaction is broadcast to a preset distributed ledger using a point-to-point broadcast protocol.

[0093] In an embodiment of the present invention, the legitimacy verification is performed by checking the validity of the signature in the Tangled transaction. After receiving the transaction, each node in the distributed ledger first verifies the legitimacy of the signature using the public key in the transaction, and then verifies the data that passes the verification at the charging pile location node. Only data that passes both tests is legal, and all others are illegal. Illegal data is marked as illegal.

[0094] In the embodiment of the present invention, the distributed ledger technology of asymmetric encryption is used to ensure the immutability and security of the charging pile data, thereby improving the trust and transparency of the system.

[0095] The resource network construction module 102 uses the unique identification of the charging pile to identify the charging pile operation data, obtains fused data, and constructs a resource sharing network based on the Internet of Things technology and the fused data.

[0096] In the embodiment of the present invention, by identifying the charging pile operation data with a unique identifier, fused data is obtained, and a resource sharing network is constructed based on the Internet of Things technology to promote resource sharing and interconnection.

[0097] In an embodiment of the present invention, when the resource network construction module performs the function of identifying the charging pile operation data according to the unique identification of the charging pile to obtain the fused data, it is specifically used to:

[0098] Performing spatiotemporal data arrangement on the charging pile operation data to obtain arrangement data;

[0099] Perform demand mapping on the arranged data to obtain dynamic demand parameter data;

[0100] Calculate the hash value of the dynamic demand parameter data to obtain the running hash;

[0101] Use the private key in the charging pile key pair to sign the running hash data to obtain the running signature data;

[0102] The unique identification data of the charging pile is embedded in the signature data to obtain fused data.

[0103] In an embodiment of the present invention, spatiotemporal data arrangement refers to adjusting or standardizing the time and space dimensions of the charging pile operation data to ensure that the data can be accurately compared and analyzed. By using the interpolation method to interpolate different timestamps to the same time coordinate axis, all data can be arranged in time.

[0104] In an embodiment of the present invention, the demand mapping is to convert the arranged data into dynamic demand parameters. More specifically, it refers to converting the data into parameters that can be understood and processed by the system based on the operating conditions of the charging pile and the needs of the user, and mapping these data to higher-level demand parameters based on the working status of the charging pile (such as the current load condition and charging demand). By converting the electrical parameters of the charging pile into a multidimensional feature vector (including derivative features such as load rate and power fluctuation coefficient), the demand is predicted on the feature vector using a pre-trained long-term and short-term demand forecasting model to obtain a dynamic demand parameter tensor within a preset minute time window. The parameter tensor is then constrained to have an output value within the range of 0 to 1 using a minimum and maximum function to obtain demand parameter data. For example, the operating data of a charging pile is mapped to power demand (for example, how much charging power is needed at a certain moment to work normally) to form dynamic demand parameter data.

[0105] In an embodiment of the present invention, the running hash is obtained by serializing dynamic parameters in a predefined format, eliminating hash inconsistencies caused by field order or encoding differences, adding a timestamp or random number, and using a preset hash function to process the standardized data to generate a running hash of a fixed length.

[0106] In an embodiment of the present invention, the operation hash is encrypted using the charging pile private key to obtain encrypted data, and the encrypted data, the charging pile public key, and the hash value are bound to form a verifiable data packet, which is the operation signature data.

[0107] In the embodiment of the present invention, the embedding is to perform serial number splicing on the unique identification of the charging pile and the signature data to obtain fused data.

[0108] In an embodiment of the present invention, by mapping the arrangement data to requirements, generating operation signature data, and embedding the signature data into the unique identification of the charging pile, it is ensured that the data of each charging pile has unique identity information, and the security of the data is enhanced to prevent the data from being tampered with.

[0109] In an embodiment of the present invention, when executing the function of constructing a resource sharing network based on Internet of Things technology and fusion data, the resource network construction module is specifically used to:

[0110] Abstract the fused data into virtual resource units;

[0111] Use the preset IoT communication protocol to connect virtual resource units and obtain idle resource data;

[0112] Perform resource health monitoring on idle resource data to obtain resource health;

[0113] Dynamically weight the idle resource data using resource health and fusion data to obtain weight data;

[0114] A network topology is constructed based on weight data and idle resource data to obtain a resource sharing network.

[0115] In an embodiment of the present invention, the data abstraction refers to parsing all fields from the fused data, clustering the fields using preset resource types to obtain resource categories, performing protocol parsing on the resource categories, and using the RDF triple model to map the fields after different protocol parsing to a unified knowledge graph. Based on the knowledge graph, a unit conversion rule library is established (such as automatically converting British temperature units to degrees Celsius), and unit conversion is processed through a finite state machine to finally obtain a virtual resource unit.

[0116] In this embodiment of the present invention, data connectivity refers to the integration of heterogeneous device data through the construction of a multi-layer communication architecture. First, at the protocol adaptation layer, standardized interfaces (such as RESTful APIs and MQTT topic trees) are automatically generated for each type of virtual resource. A dynamic encoding and decoding engine converts different industrial protocols (Modbus / OPC-UA) into a unified data model. Next, a bidirectional communication channel is established, employing a duplex transmission mechanism for real-time data transmission and reception, with sliding window flow control balancing the transmission load. Finally, dual-channel redundant transmission is implemented for critical instructions, combined with forward error correction to ensure reliability. Finally, end-to-end security protection is established through encryption using national secret algorithms and dynamic key management, forming a cross-protocol, auditable idle resource data stream, namely, idle resource data.

[0117] In this embodiment of the present invention, the resource health metric is a quantitative assessment indicator of the operating status of idle resources in the system (e.g., charging pile hardware, network, power, storage, and other available resources), reflecting the availability, reliability, and potential risks of these resources. This is achieved by defining a health threshold for each monitoring parameter in the charging pile operating data, calculating the degree of deviation from the normal range, and then normalizing this deviation to obtain a normalized degree. This normalized degree is then weighted to calculate the resource health metric.

[0118] In this embodiment of the present invention, the real-time status and health scores of resources are first integrated to construct an evaluation matrix that includes indicators such as load rate, response delay, and failure frequency. The entropy weight method is used to calculate the initial weights of each indicator and determine the basic weight coefficient. A health decay factor is then introduced to exponentially reduce the weight of abnormal nodes. In the spatiotemporal dimension, the transmission loss weight is calculated by combining the topological relationship between the resource's geographic location and the task demand point. Finally, a weighted fusion algorithm is used to integrate the static attribute weights, dynamic health adjustment factors, and environmental variables to output dynamic weight values ​​that change over time, namely weight data.

[0119] In an embodiment of the present invention, resources are first abstracted into graph nodes, whose node attributes include weight, location, and capacity; backbone nodes are screened based on a weight threshold (e.g., >0.7), and the Dijkstra algorithm is used to generate initial connections; a spatial index (e.g., an H3 grid) is established in combination with geographic constraints to ensure that physically adjacent nodes are connected preferentially; a modularity optimization algorithm is applied to divide the community structure, and finally, a dynamic topology structure is constructed through an edge weight decay method to obtain a resource sharing network.

[0120] In the embodiment of the present invention, the charging pile operation data of different charging piles are integrated and utilized to optimize the configuration and management of resources, provide users with a more efficient charging experience, and at the same time achieve interconnection between devices to improve resource utilization efficiency.

[0121] The edge computing power sharing module 103 extracts idle resources in the resource sharing network, obtains idle resource data, performs resource demand analysis on the resource sharing network, obtains resource demand data, constructs a multi-layer dynamic resource pool table based on the idle resource data, performs resource matching on the resource demand data and the multi-layer dynamic resource pool table, and obtains resource nodes.

[0122] In the embodiment of the present invention, by extracting and analyzing idle resources in a resource sharing network, a dynamic resource pool table is constructed, and resource matching is performed to achieve dynamic allocation of resources based on demand analysis and resource pool data.

[0123] In an embodiment of the present invention, the idle resource data is obtained by calculating the resource capabilities of each resource node in the resource sharing network, such as calculating the computing resources: the CPU / GPU computing power and the remaining memory capacity of the computing device in the charging pile, and the power resources: the remaining power currently allocable to the charging pile (such as the capacity of unused charging guns), etc., and screening the resource capabilities to obtain the idle resource data.

[0124] The resource demand data is obtained by extracting demand from the resource sharing network through a predefined template, and parsing natural language requests (such as the instruction "urgently need a full charge") into structured demand through a language parsing model.

[0125] See Figure 2 As shown, in the embodiment of the present invention, when the edge computing power sharing module performs the function of constructing a multi-layer dynamic resource pool table according to the idle resource data, it is specifically used to:

[0126] S21. Establish an initial resource pool based on idle resources;

[0127] S22, dividing the idle resources in the initial resource pool into multiple layers to obtain a division level;

[0128] S23. Construct a tree diagram based on the division levels to obtain a multi-layer dynamic resource pool table;

[0129] S24. Regularly update the idle resources according to a preset time period to obtain updated resources;

[0130] S25. Use the update resource to update the status of the multi-layer resource pool table to obtain a multi-layer dynamic resource pool table.

[0131] In this embodiment of the present invention, the initial resource pool automatically scans the network environment using a resource discovery protocol (such as mDNS), identifies all available physical and virtual resources (CPU, storage, devices, etc.), and collects their metadata (performance parameters, geographic location, and service type). It then performs an initial clustering based on resource attribute similarity (e.g., computing power ≥ 4 cores, memory ≥ 8GB) to establish a unified resource directory. Resource fingerprinting technology (hash algorithm) is used to generate unique identifiers, and a heartbeat mechanism is used to maintain resource online status, forming a basic resource pool.

[0132] In this embodiment of the present invention, a multi-dimensional tiering rule is constructed based on resource attributes: it is divided into a physical layer, a service layer, a geographic layer, and a priority layer. The physical layer is divided by hardware performance (e.g., GPU nodes, edge computing nodes), the service layer is grouped by functional type (real-time computing group, batch processing group), the geographic layer divides regional resource clusters based on H3 grid coding, and the priority layer is divided according to SLA levels (gold, silver, and bronze). Dynamic access policies are set for each layer (e.g., geographic layer node distance <50km), and fuzzy comprehensive evaluation is used to calculate resource adaptability, automatically adjusting the tiering granularity. Abnormal resources are isolated in an independent buffer layer.

[0133] In this embodiment of the present invention, a tree index structure is constructed based on multi-layer partitioning, comprising a root node, intermediate nodes, and leaf nodes. The root node includes a global resource scheduler, which maintains an inter-layer routing table. Intermediate nodes include resource coordinators at each layer (such as regional coordinators). Leaf nodes contain specific idle resources, resulting in a multi-layer dynamic resource pool table.

[0134] In this embodiment of the present invention, a time-driven engine is deployed, implementing a tiered update strategy. In the high-frequency layer (seconds), real-time computing resources refresh their status every 5 seconds; in the medium-frequency layer (minutes), storage resources reassess their capacity every 2 minutes; and in the low-frequency layer (hours), cold backup resources are scanned daily. The update process utilizes differential synchronization technology, transmitting only modified data (Δ updates). A sliding window algorithm is used to identify zombie resources (those with no heartbeat for 30 minutes), triggering automatic reclaim.

[0135] In an embodiment of the present invention, updated resources are inserted into leaf nodes of corresponding levels, triggering local tree rebalancing (rotation / splitting), reconstructing the tree structure during the low-peak period in the early morning every day, optimizing the query path, and implementing the status update of the multi-layer resource pool table to obtain a multi-layer dynamic resource pool table.

[0136] In the embodiment of the present invention, idle resources are effectively organized and managed to quickly adapt to changes in resource demands and maximize resource utilization.

[0137] See Figure 3 As shown, in the embodiment of the present invention, when the edge computing power sharing module performs resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain the function of the resource node, it is specifically used to:

[0138] S31. Generate matching weights based on resource demand data;

[0139] S32, screening the multi-layer dynamic resource pool table according to the division level and resource demand data of the multi-layer dynamic resource pool table to obtain candidate sub-pools;

[0140] S33. Perform weighted calculation using the matching weight and the candidate subpool to obtain a weighted score;

[0141] S34. Filter resource nodes from the candidate sub-pool according to the weight score.

[0142] In an embodiment of the present invention, the matching weight is constructed based on a weight model based on resource demand characteristics (such as computationally intensive and low-latency requirements), and the hierarchical analysis method is used to quantify the priority of each demand dimension (CPU / storage / network). The weight coefficient is dynamically adjusted in combination with the historical scheduling success rate to generate a 0-1 standardized multidimensional weight vector, providing a quantitative basis for subsequent matching.

[0143] In this embodiment of the present invention, data screening is performed layer by layer based on resource pool hierarchical rules (geographic layer / service layer): prioritizing physical proximity, then narrowing the scope by service type. Bitmap indexing is used to quickly exclude resources that do not meet hard constraints (such as inconsistent architecture types), and Bloom filters are combined to accelerate queries, outputting a subset of resources that meet basic conditions.

[0144] In this embodiment of the present invention, the weighted calculation involves performing a dot product operation on the candidate resource's attribute matrix and the weight vector, and then adding a health attenuation factor (e.g., score × 0.5 for a health score < 0.6). Entropy correction is introduced to prevent weight polarization, and the score is normalized to a percentage using the Z-Score, generating a weighted score with a confidence interval.

[0145] In an embodiment of the present invention, the resource node screening is performed by arranging candidate nodes in descending order of scores to implement diversity control: selecting TopN nodes (N = required number of replicas × 3), excluding redundant nodes in the same fault domain based on the anti-affinity strategy, and finally outputting the optimal node set while retaining suboptimal nodes as elastic alternatives.

[0146] In the embodiment of the present invention, by accurately matching the extraction of idle resources with resource requirements, resource allocation is optimized, resource waste is reduced, and resource utilization efficiency is improved. This dynamic adjustment capability also improves the flexibility and response speed of the system.

[0147] The charging power allocation module 104 is used to distribute data to resource nodes according to the resource sharing network to obtain distribution data, calculate the charging pile power based on the distribution data using the resource nodes to obtain allocated power, and allocate power to the charging piles according to the allocated power to obtain a charging pile power allocation result.

[0148] In this embodiment of the present invention, based on a resource-sharing network, the module can distribute power to ensure that each charging station receives the required charging power. With the support of resource nodes, the power requirements of the charging stations are accurately calculated and allocated, avoiding excessive consumption of power resources.

[0149] In an embodiment of the present invention, when the charging power allocation module performs the function of distributing data to resource nodes according to the resource sharing network to obtain distributed data, it is specifically configured to:

[0150] Generate a distribution path priority list based on the resource sharing network;

[0151] Encapsulate the fused data corresponding to the resource demand data to obtain a standardized data packet;

[0152] Based on the distribution path priority list, the standardized data packet is pushed to the resource node to obtain the distribution data.

[0153] In an embodiment of the present invention, based on the graph structure of the resource sharing network, an improved Dijkstra algorithm is used to calculate the shortest path between nodes in the resource sharing network, and a distribution path priority list is generated in combination with real-time network quality indicators (delay, packet loss rate, bandwidth utilization).

[0154] In an embodiment of the present invention, resource demand data is processed in layers according to the protocol stack: metadata (timestamp, traceability hash) is injected into the application layer, the encryption layer adopts the SM4 national encryption algorithm, the compression layer uses DEFLATE to reduce the volume, and the transport layer encapsulates the QUIC message header to generate a lightweight, interference-resistant standardized data packet.

[0155] In the embodiment of the present invention, when the standardized data packet is successfully pushed to the resource node and processed or directly used as input for subsequent processes (such as calculating the allocated power), this part of the data becomes the distribution data.

[0156] In the embodiment of the present invention, by optimizing the data distribution path, data transmission time and delay are reduced, thereby improving the operating efficiency of the entire system.

[0157] In an embodiment of the present invention, when the charging power allocation module performs the function of calculating the charging pile power based on the distributed data using the resource node to obtain the allocated power, it is specifically configured to:

[0158] Decrypt the digital signature in the distribution data using the public key corresponding to the resource node in the distribution data to obtain the decryption result;

[0159] Perform data verification on the decryption result, and perform data parsing on the distribution data that passes the data verification to obtain parsed data;

[0160] The distributed power is calculated based on the preset grid dynamic attenuation coefficient and analytical data.

[0161] In the embodiment of the present invention, an asymmetric encryption system is adopted to decrypt the data signature in the distributed data using the public key preset by the resource node to obtain a decryption result.

[0162] In the embodiment of the present invention, the allocated power is calculated using the following formula:

[0163]

[0164] in, For the The allocated power corresponding to each charging pile is: is the preset grid dynamic attenuation coefficient, To analyze the data The required power corresponding to each charging pile is: In order to analyze the load value of the real-time monitoring of the power grid in the data, In order to analyze the upper limit load value of the safe operation of the power grid in the data, To analyze the total power that the power grid can currently provide in the data.

[0165] In the embodiment of the present invention, the To adjust the factor, dynamic load adjustment is achieved, grid load is included in the distribution logic, high load , indicating enhanced attenuation, reduced power distribution, preventing overload, low load , which means that the attenuation is weakened and the on-demand allocation is maximized.

[0166] In an embodiment of the present invention, the recovered hash value is compared with the hash value of the current data (i.e., the decrypted distribution data content) calculated by the receiving end according to the same algorithm. If the two match, it indicates that the data has not been modified during transmission and indeed comes from the sender who owns the corresponding private key. Once the data is successfully verified, the next step is to convert it from the transmission format (such as an encrypted message, encoded string, etc.) into a structured format that the application can understand and use (such as a JSON object, XML document, or other programming language-specific data structure), and extract the required parameters or variable values ​​from the parsed data structure. For example, in the charging pile power distribution scenario, this may include but is not limited to information such as the charging pile identifier, the requested charging time, and the required power.

[0167] In the embodiment of the present invention, by considering the required power of each charging pile and the real-time status of the power grid (including the load value and the maximum safe operating load), the total power of the power grid can be more reasonably distributed to each charging pile, ensuring that the needs of each charging pile are met while maximizing the efficiency of the power grid.

[0168] In the embodiment of the present invention, when the charging power allocation module performs the function of allocating power to the charging pile according to the allocated power and obtaining the charging pile power allocation result, it is specifically used to:

[0169] Encapsulate the allocated power into instructions to obtain control instructions;

[0170] Upload the control instruction to the node corresponding to the resource demand data, and the node parses the control instruction to obtain the parsed instruction;

[0171] According to the parsing instructions, the power of the nodes corresponding to the resource demand data is adjusted to obtain the charging pile allocation result.

[0172] In this embodiment of the present invention, the allocated power value is converted into a device-executable instruction using Protobuf binary encoding, adding the target node ID, the effective time window (start and end timestamps), and the execution priority tag. A CRC32 checksum is embedded in the instruction header, and sensitive parameters are encrypted using the national SM4 algorithm. A standard control instruction packet with a digital signature is generated to obtain the control instruction.

[0173] In an embodiment of the present invention, the data is pushed to the target node via an IoT protocol (such as MQTT or QoS2 protocol), and an instruction parsing engine is deployed on the node side: the validity of the digital signature and timestamp is first verified, and the parsed instruction is obtained after decryption. If an abnormal instruction is obtained (verification failure / timeout), a retransmission mechanism is triggered.

[0174] In this embodiment of the present invention, the node power controller receives parsed commands and uses a PID algorithm to close the loop and adjust the IGBT module output. It monitors voltage and current changes in real time and provides feedback (error <±1%) to adjust the deviation. It uses dual PWM signal redundant outputs, with millisecond-level switching between the primary and backup channels. Ultimately, it generates a timestamped power allocation result log, which is synchronously updated to the resource pool status table.

[0175] Furthermore, the power controller in the node receives the analytical instructions and uses a PID (proportional-integral-derivative) algorithm to perform closed-loop regulation of the IGBT (insulated-gate bipolar transistor) module output. The PID algorithm monitors voltage and current changes in real time and uses this feedback to adjust deviations, ensuring that the output power precisely meets the required level. This process maintains an error within ±1%, ensuring highly accurate power regulation.

[0176] In the embodiment of the present invention, through precise power distribution, insufficient or wasted power of the charging pile is avoided, the utilization rate of electric power resources is improved, and the charging efficiency of the charging pile and user satisfaction are improved.

[0177] like Figure 4 FIG. 1 is a flow chart of a method for controlling a charging pile based on the Internet of Things according to an embodiment of the present invention. In an embodiment of the present invention, the method for controlling a charging pile based on the Internet of Things includes:

[0178] S401. Obtain charging pile data, split the charging pile data into charging pile operation data and charging pile identification data, generate a charging pile hardware identification based on the charging pile identification data, digitally bind the charging pile hardware identification based on an asymmetric encrypted distributed ledger, and obtain a unique charging pile identification;

[0179] S402: Use the unique identifier of the charging pile to identify the charging pile operation data to obtain fused data, and build a resource sharing network based on the Internet of Things technology and the fused data;

[0180] S403: Extract idle resources from the resource sharing network to obtain idle resource data, perform resource demand analysis on the resource sharing network to obtain resource demand data, construct a multi-layer dynamic resource pool table based on the idle resource data, perform resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain resource nodes;

[0181] S404. Distribute data to resource nodes according to the resource sharing network to obtain distribution data, calculate charging pile power using the resource nodes to obtain allocated power, and distribute power to the charging piles according to the allocated power to obtain a charging pile power allocation result.

[0182] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology.

[0183] Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0184] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system may also be implemented by a single unit or system through software or hardware. Terms such as first and second are used to indicate names and do not imply any particular order.

[0185] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A charging pile control system based on the Internet of Things, characterized in that: The system includes: a charging pile authentication module, a resource network construction module, an edge computing power sharing module, and a charging power distribution module. Specifically: A charging pile authentication module is configured to obtain charging pile data, split the charging pile data into charging pile operation data and charging pile identification data, normalize the charging pile identification data to obtain normalized data, perform hierarchical hash calculation on the normalized data to obtain hierarchical data, perform XOR fusion on the hierarchical data to obtain the charging pile hardware identification, digitally sign the charging pile hardware identification using the private key in a preset charging pile key pair to obtain signature data, construct a tangle transaction based on the signature data and the public key in the charging pile key pair, broadcast the tangle transaction to all nodes in a preset distributed ledger network to obtain an updated ledger, verify the legitimacy of the charging pile hardware identification using the public key in the charging pile key pair, the updated ledger, and the tangle transaction to obtain a verification result, filter out legitimate transactions from the tangle transactions based on the verification result, calculate the hash value of the legitimate transactions to obtain a tangle hash, and use the tangle hash as the unique identification of the charging pile; The resource network construction module uses the unique identification of the charging pile to identify the charging pile operation data, obtains fused data, and builds a resource sharing network based on the Internet of Things technology and fused data; The edge computing power sharing module extracts idle resources from the resource sharing network, obtains idle resource data, analyzes resource demand on the resource sharing network, obtains resource demand data, builds a multi-layer dynamic resource pool table based on the idle resource data, and performs resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain resource nodes. The charging power distribution module is used to distribute data to resource nodes based on the resource sharing network and resource demand data to obtain distribution data, decrypt the digital signature in the distribution data using the public key corresponding to the resource node in the distribution data to obtain the decryption result; perform data verification on the decryption result, and perform data analysis on the distribution data that passes the data verification to obtain the analysis data; calculate the distribution power based on the preset power grid dynamic attenuation coefficient and the analysis data, and calculate the distribution power using the following formula: Among them, P i is the allocated power corresponding to the i-th charging pile, ∝ is the preset grid dynamic attenuation coefficient, D i To analyze the required power corresponding to the i-th charging pile in the data, L currunt To analyze the load value of the power grid in real time monitoring data, L max In order to analyze the upper limit load value of the safe operation of the power grid in the data, P total In order to analyze the total power currently available from the power grid in the data, the power of the charging piles is allocated according to the allocated power to obtain the power allocation result of the charging piles; When the resource network construction module performs the function of identifying the charging pile operation data using the charging pile unique identifier to obtain fused data, it is specifically used to: Performing spatiotemporal data arrangement on the charging pile operation data to obtain arrangement data; Perform demand mapping on the arrangement data to obtain dynamic demand parameter data; Calculate the hash value of the dynamic demand parameter data to obtain the running hash; Use the private key in the charging pile key pair to sign the running hash data to obtain the running signature data; The unique identification data of the charging pile is embedded in the signature data to obtain fused data.

2. The charging pile control system based on the Internet of Things as claimed in claim 1, characterized in that: When executing the function of constructing a resource sharing network based on Internet of Things technology and fused data, the resource network construction module is specifically used to: Abstract the fused data into virtual resource units; Use the preset Internet of Things communication protocol to connect the virtual resource units to obtain the connected data; Perform resource health monitoring on Unicom data to obtain resource health status; Dynamically calculate the weight of Unicom data based on resource health and fusion data to obtain weight data; The network topology is constructed based on the weight data and the connection data to obtain a resource sharing network.

3. The charging pile control system based on the Internet of Things according to claim 1, characterized in that: When executing the function of constructing a multi-layer dynamic resource pool table based on idle resource data, the edge computing power sharing module is specifically used to: Establish an initial resource pool based on idle resources; Perform multi-layer division on the idle resources in the initial resource pool to obtain division levels; A tree diagram is constructed based on the division levels to obtain a multi-layer resource pool table; Regularly update idle resources according to a preset time period to obtain updated resources; The multi-layer resource pool table is updated with updated resources to obtain a multi-layer dynamic resource pool table.

4. The charging pile control system based on the Internet of Things according to claim 3, characterized in that: When performing resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain the function of the resource node, the edge computing power sharing module is specifically used to: Generate matching weights based on resource demand data; Screen the multi-layer dynamic resource pool table according to its division levels and resource demand data to obtain candidate sub-pools; Use the matching weight and candidate subpool to perform weighted calculation to obtain a weighted score; Resource nodes are selected from the candidate sub-pool based on weight scores.

5. The charging pile control system based on the Internet of Things according to claim 1, characterized in that: When the charging power distribution module performs the function of distributing data to resource nodes according to the resource sharing network and resource demand data to obtain distribution data, it is specifically used to: Generate a distribution path priority list based on the resource sharing network; Encapsulate the fused data corresponding to the resource demand data to obtain a standardized data packet; Based on the distribution path priority list, the standardized data packet is pushed to the resource node to obtain the distribution data.

6. The charging pile control system based on the Internet of Things according to claim 1, characterized in that: When the charging power allocation module performs the function of allocating power to the charging pile according to the allocated power and obtaining the charging pile power allocation result, it is specifically used to: Encapsulate the allocated power into instructions to obtain control instructions; The control instruction is uploaded to the charging pile node corresponding to the resource demand data, and the charging pile node parses the control instruction to obtain the parsed instruction; According to the parsing instructions, the power of the charging pile node corresponding to the resource demand data is adjusted to obtain the charging pile power allocation result.

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