Charging pile control system based on Internet of Things

Abstract

The invention relates to the technical field of the Internet of Things, and discloses a charging pile control system based on the Internet of Things, and the system comprises the steps: generating a charging pile hardware identifier according to charging pile identifier data, and carrying out the digital binding of the charging pile hardware identifier, and obtaining a charging pile unique identifier; performing data identification on the charging pile operation data by using the charging pile unique identifier to obtain fusion data, and constructing a resource sharing network based on the fusion data; the method comprises the following steps: extracting idle resources and resource demand data in a resource sharing network, constructing a multi-layer dynamic resource pool table according to the idle resource data, and performing resource matching on the resource demand data and the dynamic resource pool table to obtain resource nodes; and performing data distribution on the resource nodes according to the resource sharing network to obtain distribution data, and performing charging pile resource calculation on the distribution data by using the resource nodes to obtain distribution power. According to the invention, the problem that the current charging pile control system still has insufficient control accuracy in charging pile resource allocation can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of the 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 progress, and the enhancement of consumers' environmental awareness, the number of electric vehicles globally has also increased explosively in recent years. The popularization of electric vehicles has further brought about a sharp increase in charging demand.

[0003] Currently, many charging piles still adopt traditional management methods. However, traditional charging pile systems rely on centralized authentication and management methods, which means that the control and data storage of the entire system are concentrated on a central node. If this central node is attacked, it may lead to large-scale service interruptions or data tampering (such as false reporting of charging amounts), thus affecting the security and trust of the system; moreover, the existing power distribution of charging piles mostly adopts static strategies, that is, the power distribution of each charging pile is preset and cannot be dynamically adjusted. With the popularization of electric vehicles and the growth of charging demand, static strategies cannot flexibly respond to demand changes at different times. This may lead to overloading of local charging piles, idle resources of some charging piles, or the inability of some charging piles to meet the charging needs of electric vehicles during high demand, reducing the resource utilization efficiency. In summary, the current charging pile control system still has problems of 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, and its main purpose is to solve the problems of low resource allocation efficiency and insufficient data security existing in the current charging pile control system.

[0005] To achieve the above object, a charging pile control system based on the Internet of Things provided by the present invention includes: A charging pile authentication module, a resource network construction module, an edge computing power sharing module, and a charging power distribution module. Specifically: 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 a charging pile hardware identifier according to the charging pile identification data, and perform digital binding on the charging pile hardware identifier based on a distributed ledger of asymmetric encryption to obtain a unique charging pile identifier; The resource network construction module uses the unique charging pile identifier to perform data identification on the charging pile operation data to obtain fusion data, and constructs a resource sharing network based on the Internet of Things technology and the fusion data; The edge computing power sharing module extracts the idle resources in the resource sharing network to obtain idle resource data, conducts resource demand analysis on the resource sharing network to obtain resource demand data, constructs 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 the resource nodes according to the resource sharing network and the resource demand data to obtain distributed data, calculate the charging pile power using the resource nodes for the distributed data to obtain the allocated power, and perform power distribution on the charging piles according to the allocated power to obtain the charging pile power distribution result.

[0006] Optionally, when the charging pile authentication module executes the function of generating the charging pile hardware identifier according to the charging pile identification data, it specifically is used for: Normalize the charging pile identification data to obtain normalized data; Perform hierarchical hashing calculation on the normalized data to obtain hierarchical data; Perform exclusive-or fusion on the hierarchical data to obtain the charging pile hardware identifier.

[0007] Optionally, when the charging pile authentication module executes the function of digitally binding the charging pile hardware identifier based on the asymmetric encryption distributed ledger to obtain the unique charging pile identifier, it specifically is used for: Use the private key in the preset charging pile key pair to digitally sign the charging pile hardware identifier to obtain signature data; Construct a tangle transaction according to the signature data and the public key in the charging pile key pair; Broadcast the tangle transaction to all nodes of the preset distributed ledger network to obtain the updated ledger; 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 to obtain a verification result; Screen out legal transactions from the tangle transactions according to the verification result, calculate the hash value of the legal transactions to obtain the tangle hash, and use the tangle hash as the unique charging pile identifier.

[0008] Optionally, when the resource network construction module executes the function of data-identifying the charging pile operation data using the unique charging pile identifier to obtain fusion data, it specifically is used for: Arrange the charging pile operation data in space-time data to obtain arranged data; Perform demand mapping on the arranged data to obtain dynamic demand parameter data; Calculate the hash value of the dynamic demand parameter data to obtain the operation hash; Use the private key in the charging pile key pair to digitally sign the operation hash to obtain the operation signature data; Embed the unique identification data of the charging pile into the signature data to obtain the fusion data.

[0009] Optionally, when the resource network construction module executes the function of constructing a resource sharing network based on the Internet of Things technology and the fusion data, it is specifically used for: Abstract the fusion data into virtual resource units; Use a preset Internet of Things communication protocol to connect the virtual resource units for data communication to obtain connected data; Conduct resource health monitoring on the connected data to obtain the resource health level; Perform dynamic weight calculation on the connected data based on the resource health level and the fusion data to obtain weight data; Construct a network topology diagram based on the weight data and the connected data to obtain a resource sharing network.

[0010] Optionally, when the edge computing power sharing module executes the function of constructing a multi-layer dynamic resource pool table according to the idle resource data, it is specifically used for: Establish an initial resource pool according to the idle resources; Perform multi-layer partitioning on the idle resources in the initial resource pool to obtain the partitioning levels; Construct a tree diagram according to the partitioning levels to obtain a multi-layer resource pool table; Perform resource timing update on the idle resources according to a preset time period to obtain updated resources; Use the updated resources to update the status of the multi-layer resource pool table to obtain a multi-layer dynamic resource pool table.

[0011] Optionally, when the edge computing power sharing module executes the function of performing resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain resource nodes, it is specifically used for: Generate a matching weight according to the resource demand data; Perform data screening on the multi-layer dynamic resource pool table according to the partitioning levels of the multi-layer dynamic resource pool table and the resource demand data to obtain candidate sub-pools; Perform weighted calculation using the matching weight and the candidate sub-pools to obtain a weight score; Screen out resource nodes from the candidate sub-pools according to the weight score.

[0012] Optionally, when the charging power distribution module executes the function of performing data distribution on the resource nodes according to the resource sharing network and the resource demand data to obtain distribution data, it is specifically used for: Generate a distribution path priority list according to the resource sharing network; Encapsulate the fusion data corresponding to the resource demand data to obtain a standardized data packet; Push the standardized data packet to the resource node based on the distribution path priority list to obtain the distribution data.

[0013] Optionally, when the charging power distribution module executes the function of calculating the charging pile power from the distribution data using the resource node to obtain the allocated power, it specifically is used for: Use the public key corresponding to the resource node in the distribution data to decrypt the digital signature in the distribution data to obtain the decryption result; Perform data verification on the decryption result, and perform data parsing on the distribution data that passes the data verification to obtain the parsed data; Calculate the allocated power according to the preset grid dynamic attenuation coefficient and the parsed data.

[0014] Optionally, when the charging power distribution module executes the function of performing power distribution on the charging pile according to the allocated power to obtain the charging pile power distribution result, it specifically is used for: Perform instruction encapsulation on the allocated power to obtain a control instruction; Upload the control instruction 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; Perform power adjustment on the charging pile node corresponding to the resource demand data according to the parsed instruction to obtain the charging pile power distribution result.

[0015] In the present invention, a hardware identifier is generated for the charging pile data, and the identifier is digitally bound to the hardware identifier using an asymmetric encryption distributed ledger, avoiding single point of failure and supporting cross-region charging pile identity mutual trust. In addition, by calculating the dynamic attenuation coefficient according to the real-time load of the power grid and performing power distribution according to the dynamic attenuation coefficient, it can quickly respond to the demand changes in different time periods. Therefore, a charging pile control system based on the Internet of Things proposed by the present invention can effectively solve the problem of low resource allocation efficiency still existing in the current charging pile control system. Description of the Drawings

[0016] Figure 1 It is a functional module diagram of a charging pile control system based on the Internet of Things provided by an embodiment of the present invention; Figure 2 It is a schematic flow diagram of constructing a multi-layer dynamic resource pool table provided by an embodiment of the present invention; Figure 3 It is a schematic flow diagram of resource matching provided by an embodiment of the present invention; Figure 4 It is a schematic flow diagram of a charging pile control method based on the Internet of Things provided by an embodiment of the present invention.

[0017] The realization, functional features, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed implementation mode

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] Refer to Figure 1 As shown, it is a functional module diagram of a charging pile control system based on the Internet of Things provided by an 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 realized, the charging pile control system 100 based on the Internet of Things can 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 modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0020] 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 identifier according to the charging pile identification data, and digitally binding the charging pile hardware identifier based on an asymmetric encryption distributed ledger to obtain a unique charging pile identifier; In the embodiment of the present invention, the resource network construction module 102 includes using the unique charging pile identifier to perform data identification on the charging pile operation data to obtain fusion data, and constructing a resource sharing network based on the Internet of Things technology and the fusion data; In the 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 according to the idle resource data, and performing resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain resource nodes; In the embodiment of the present invention, the charging power distribution module 104 includes performing data distribution on the resource nodes according to the resource sharing network to obtain distribution data, using the resource nodes to calculate the charging pile power for the distribution data to obtain the allocated power, and performing power distribution on the charging pile according to the allocated power to obtain the charging pile power distribution result.

[0021] Specifically, in the embodiment of the present invention, each module in the charging pile control system 100 based on the Internet of Things uses the same technical means as the charging pile control system based on the Internet of Things described in the accompanying drawings and can produce the same technical effects, which will not be elaborated here.

[0022] The following will describe each component and the specific working process of the above-mentioned charging pile control system based on the Internet of Things in combination with specific embodiments: 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 identifier according to the charging pile identification data, and perform digital binding on the charging pile hardware identifier based on an asymmetric encryption distributed ledger to obtain a unique charging pile identifier.

[0023] In the embodiment of the present invention, by obtaining 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.

[0024] In the embodiment of the present invention, the charging pile data refers to various information related to the charging pile, including but not limited to the operating status, working parameters, device identifier, and other performance data of the charging pile.

[0025] In the embodiment of the present invention, the charging pile operation data is mainly related to the performance and working status of the charging pile, specifically including real-time monitored charging pile operation data, power, charging duration, and other data; the charging pile identification data contains key information for identifying the identity of the charging pile, such as hardware identifier, device ID, and location information.

[0026] In the embodiment of the present invention, when the charging pile authentication module executes the function of generating a charging pile hardware identifier according to the charging pile identification data, it specifically is used for: Performing normalization processing on the charging pile identification data to obtain normalized data; Performing hierarchical hashing calculation on the normalized data to obtain hierarchical data; Performing exclusive OR fusion on the hierarchical data to obtain a charging pile hardware identifier.

[0027] In the embodiment of the present invention, the normalization processing is to perform binary conversion on the identification ID in the charging pile identification data to obtain a binary string, and perform sliding window filtering on the location information data in the charging pile identification data. Wherein, the window size is equal to 50 samplings, and the outliers exceeding three standard value ranges in the sampling result are removed to obtain filtered data, and the filtered data and the binary string are stored in the form of a data set to obtain normalized data.

[0028] In the embodiment of the present invention, the hierarchical hashing refers to calculating the hash value of 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 dimensionality-reduced data, and adding the dimensionality-reduced data and the hash data to an empty data set to obtain hierarchical data.

[0029] In an embodiment of the present invention, the dimensionality-reduced data and the hash data in the hierarchical data are XOR-added to obtain XOR data, the acquisition time and geographical location of the charging pile identification data are obtained, and an OR operation is performed according to the acquisition time, the geographical location, and the XOR data to obtain an operation result, and the operation result is subjected to a hash calculation to obtain a charging pile hardware identifier.

[0030] In an embodiment of the present invention, by performing hierarchical encryption and non-linear fusion on the charging pile identification data to generate a unique identifier, it can be ensured that any single data being tampered with will result in an abnormal fusion result, enhancing the overall credibility.

[0031] In an embodiment of the present invention, when the charging pile authentication module executes the function of digitally binding the charging pile hardware identifier based on the asymmetric encryption-based distributed ledger to obtain a unique charging pile identifier, it specifically is used for: Using the private key in the preset charging pile key pair to digitally sign the charging pile hardware identifier to obtain signature data; Constructing a tangle transaction according to the signature data and the public key in the charging pile key pair; Using the public key in the charging pile key pair and the tangle transaction to verify the legitimacy of the charging pile hardware identifier to obtain a verification result; Broadcasting the tangle transaction to all nodes of the preset distributed ledger network to obtain an updated ledger; Using 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 to obtain a verification result; According to the verification result, screening out legal transactions from the tangle transaction, calculating the hash value of the legal transaction to obtain a tangle hash, and using the tangle hash as the unique charging pile identifier.

[0032] In an embodiment of the present invention, the preset charging pile key pair refers to an SM2 elliptic curve key pair locally generated 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 every preset time period through a preset key derivation function.

[0033] In an embodiment of the present invention, characteristic data of the charging pile is generated according to the charging pile hardware identifier, and the characteristic data of the charging pile is signed using the private key and a signature algorithm to obtain signature data.

[0034] 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.

[0035] In an embodiment of the present invention, when the charging pile completes the signature of the hardware identifier through the private key and constructs a tangle transaction containing signature data, public key, and other metadata (timestamp, transaction type, etc.), the tangle transaction is broadcast to a preset distributed ledger by using a peer-to-peer broadcast protocol.

[0036] In an embodiment of the present invention, the legality verification is performed by verifying the validity of the signature in the tangle transaction. After each node in the distributed ledger receives the transaction, it first verifies the legality of the signature with the public key in the transaction, and verifies the charging pile location node for the data that passes the verification. Only the data that passes both verifications is legal, and the others are illegal. The illegal data is marked as illegal.

[0037] In an embodiment of the present invention, the distributed ledger technology using asymmetric encryption ensures the immutability and security of the charging pile data, and improves the trust and transparency of the system.

[0038] The resource network construction module 102 uses the unique identifier of the charging pile to perform data identification on the operation data of the charging pile, obtains the fusion data, and constructs a resource sharing network based on the Internet of Things technology and the fusion data.

[0039] In an embodiment of the present invention, by performing data identification on the operation data of the charging pile and the unique identifier, the fusion data is obtained, and a resource sharing network is constructed based on the Internet of Things technology, promoting resource sharing and interconnection.

[0040] In an embodiment of the present invention, when the resource network construction module executes the function of performing data identification on the operation data of the charging pile by using the unique identifier of the charging pile to obtain the fusion data, it specifically is used for: Performing spatio-temporal data arrangement on the operation data of the charging pile to obtain arranged data; Performing demand mapping on the arranged data to obtain dynamic demand parameter data; Calculating the hash value of the dynamic demand parameter data to obtain the operation hash; Using the private key in the charging pile key pair to perform data signature on the operation hash to obtain operation signature data; Embedding the unique identifier data of the charging pile into the signature data to obtain the fusion data.

[0041] In an embodiment of the present invention, the spatio-temporal data arrangement refers to adjusting or standardizing according to the time and space dimensions of the operation data of the charging pile to ensure that the data can be accurately compared and analyzed. By using the interpolation method to interpolate different timestamps onto the same time coordinate axis, the time arrangement of all data is realized.

[0042] In the embodiments of the present invention, the demand mapping is to convert the arranged data into dynamic demand parameters. Further, it refers to converting the data according to the operating conditions of the charging piles and the needs of users into parameters that the system can understand and process. According to the working status of the charging piles (such as the current load condition and charging demand), these data are mapped to higher-level demand parameters. By converting the electrical parameters of the charging piles into multi-dimensional feature vectors (including derivative features such as load factor and power fluctuation coefficient), and then using a pre-trained long-term and short-term demand prediction model to perform demand prediction on the feature vectors, a dynamic demand parameter tensor within a preset minute time window is obtained. Then, the parameter tensor is constrained within the range of 0 to 1 using the min-max function to obtain demand parameter data. For example, the operating data of a certain charging pile is mapped to power demand (for example: how much charging power is required to work properly at a certain moment), forming dynamic demand parameter data.

[0043] In the embodiments of the present invention, the running hash is obtained by serializing the dynamic parameters in a predefined format to eliminate hash inconsistencies caused by field order or encoding differences, and then adding a timestamp or random number to obtain standardized data. A preset hash function is used to process the standardized data to generate a running hash with a fixed length.

[0044] In the embodiments of the present invention, the private key of the charging pile is used to perform an encryption operation on the running hash to obtain encrypted data. The encrypted data, the public key of the charging pile, and the hash value are bound to form a verifiable data packet, which is the running signature data.

[0045] In the embodiments of the present invention, the embedding is to splice the unique identifier of the charging pile and the signature data in sequence to obtain fused data.

[0046] In the embodiments of the present invention, by performing demand mapping on the arranged data, generating running signature data, and embedding the signature data with the unique identifier 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 data tampering.

[0047] In the embodiments of the present invention, when the resource network construction module executes the function of constructing a resource sharing network based on the Internet of Things technology and the fused data, it specifically is used for: Abstracting the fused data into virtual resource units; Using a preset Internet of Things communication protocol to connect the virtual resource units for data communication to obtain idle resource data; Performing resource health monitoring on the idle resource data to obtain the resource health degree; Performing dynamic weight calculation on the idle resource data using the resource health degree and the fused data to obtain weight data; Construct a network topology diagram based on the weight data and idle resource data to obtain a resource sharing network.

[0048] In the embodiment of the present invention, the data abstraction refers to parsing all fields from the fusion data, clustering the fields using a preset resource type to obtain resource categories, parsing the protocols of the resource categories, adopting the RDF triple model, mapping the fields after different protocol parses to a unified knowledge graph, establishing a unit conversion rule library according to the knowledge graph (such as automatically converting the imperial temperature unit to Celsius), processing unit conversion through a finite state machine, and finally obtaining virtual resource units.

[0049] In the embodiment of the present invention, data connection refers to realizing data integration of heterogeneous devices by constructing a multi-layer communication architecture. First, at the protocol adaptation layer, a standardized interface (such as RESTful API, MQTT topic tree) is automatically generated for each type of virtual resource, and different industrial protocols (Modbus / OPC-UA) are converted into a unified data model through a dynamic encoding and decoding engine. Then, a two-way communication channel is established, and a duplex transmission mechanism is used to send and receive data in real time, and the transmission load is balanced through a sliding window flow control. Then, dual-channel redundant transmission is adopted for key instructions, and forward error correction is combined to ensure reliability. Finally, end-to-end security protection is constructed through national cryptographic algorithm encryption and dynamic key management to form an idle resource data stream that crosses protocols and is auditable, that is, idle resource data.

[0050] In the embodiment of the present invention, the resource health degree is a quantitative evaluation index for the operating state of idle resources in the system (such as available resources such as the hardware, network, power, and storage of charging piles), which is used to reflect the availability, reliability, and potential risks of resources. By defining a health threshold for each monitoring parameter in the operating data of the charging pile, calculating the degree of deviation from the normal range, normalizing the degree of deviation from the normal range to obtain a normalized degree, and performing a weighted calculation on the normalized degree to obtain the resource health degree.

[0051] In the embodiment of the present invention, first, integrate the real-time state of resources and the health score, and construct an evaluation matrix including indicators such as load rate, response delay, and failure frequency. Use the entropy weight method to calculate the initial weights of each indicator to determine the basic weight coefficient. Subsequently, introduce a health attenuation factor to perform exponential weight reduction on abnormal nodes. In the space-time dimension, combine the topological relationship between the geographical location of resources and the task demand points to calculate the transmission loss weight. Finally, through a weighted fusion algorithm, comprehensively combine the static attribute weight, dynamic health adjustment factor, and environmental variables to output a dynamic weight value that changes with time, that is, weight data.

[0052] In the embodiments of the present invention, resources are first abstracted as graph nodes, and the node attributes include weight, location, and capacity. Based on a weight threshold (e.g., >0.7), backbone nodes are screened, and the Dijkstra algorithm is used to generate an initial connection. A spatial index (such as an H3 grid) is established in combination with geographical constraints to ensure that physically adjacent nodes are preferentially connected. The modularity optimization algorithm is applied to divide the community structure, and finally, a dynamic topology structure is constructed through the edge weight attenuation method to obtain a resource sharing network.

[0053] In the embodiments of the present invention, the charging pile operation data of different charging piles is integrated and utilized to optimize the configuration and management of resources, provide a more efficient charging experience for users, and at the same time achieve interconnection between devices and improve the utilization efficiency of resources.

[0054] The edge computing power sharing module 103 extracts idle resources in the resource sharing network to obtain idle resource data, conducts a resource demand analysis on the resource sharing network to obtain resource demand data, constructs 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.

[0055] In the embodiments of the present invention, by extracting and analyzing idle resources in the resource sharing network, constructing a dynamic resource pool table, and performing resource matching, dynamic allocation of resources is realized based on demand analysis and resource pool data.

[0056] In the embodiments 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 computing resources: the CPU / GPU computing power and the remaining memory capacity of the computing devices in the charging pile, and power resources: the remaining power that can be allocated by the charging pile currently (such as the capacity of the unused charging gun), and screening the resource capabilities to obtain the idle resource data.

[0057] The resource demand data is obtained by extracting the demands of the resource sharing network through a predefined template, and parsing the natural language request (such as the instruction "urgently need to be fully charged") into a structured demand through a language parsing model to obtain the resource demand data.

[0058] See Figure 2 As shown, in the embodiments of the present invention, when the edge computing power sharing module executes the function of constructing a multi-layer dynamic resource pool table according to the idle resource data, it specifically is used for: S21. Establish an initial resource pool according to the idle resources; S22. Perform multi-layer division on the idle resources in the initial resource pool to obtain division levels; S23. Construct a tree diagram according to the division levels to obtain a multi-layer dynamic resource pool table; S24. Perform resource timing update on idle resources according to a preset time period to obtain updated resources; S25. Use the updated resources to update the status of the multi-layer resource pool table to obtain a multi-layer dynamic resource pool table.

[0059] In the embodiments of the present invention, the initial resource pool automatically scans the network environment through a resource discovery protocol (such as mDNS), identifies all idle physical / virtual resources (CPU, storage, devices, etc.), and collects their metadata (performance parameters, geographical location, service type). Based on the similarity of resource attributes (such as computing power ≥ 4 cores, memory ≥ 8GB), the first clustering is performed to establish a unified resource directory. The resource fingerprint technology (hash algorithm) is used to generate a unique identifier, and the online status of the resources is maintained through a heartbeat mechanism to form a basic resource pool.

[0060] In the embodiments of the present invention, hierarchical rules are constructed based on multiple dimensions of resource attributes: divided into a physical layer, a service layer, a geographical layer, and a priority layer. Among them, the physical layer is divided according to hardware performance (such as GPU nodes, edge computing nodes), the service layer is grouped according to functional types (real-time computing group, batch processing group), the geographical layer divides regional resource clusters based on H3 grid coding, and the priority layer is divided according to SLA levels (gold / silver / bronze levels). A dynamic admission policy is set for each layer (such as the node distance in the geographical layer < 50km), the fuzzy comprehensive evaluation method is used to calculate the resource adaptation degree, and the hierarchical granularity is automatically adjusted; abnormal resources are isolated to an independent buffer layer.

[0061] In the embodiments of the present invention, based on the multi-layer division, a tree index structure is constructed, including a root node, intermediate nodes, and leaf nodes. The root node includes a global resource scheduler that maintains an inter-layer routing table. The intermediate nodes include resource coordinators at each level (such as regional coordinators). The leaf nodes include specific idle resources to obtain a multi-layer dynamic resource pool table.

[0062] In the embodiments of the present invention, a time-driven engine is deployed to implement a hierarchical update strategy, where the high-frequency layer (second level): the status of real-time computing resources is refreshed every 5 seconds, the medium-frequency layer (minute level): the capacity of storage resources is re-evaluated every 2 minutes, and the low-frequency layer (hour level): cold backup resources are scanned daily. The differential synchronization technology is used in the update process, and only the changed data (Δ update) is transmitted. Zombie resources are identified through a sliding window algorithm (no heartbeat for 30 minutes), and automatic recovery is triggered.

[0063] In the embodiments of the present invention, the updated resources are inserted into the corresponding leaf nodes at each level, triggering local tree rebalancing (rotation / splitting). The tree structure is reconstructed during the low-peak period at midnight every day to optimize the query path and realize the status update of the multi-layer resource pool table to obtain a multi-layer dynamic resource pool table.

[0064] In the embodiments of the present invention, by effectively organizing and managing idle resources, it can quickly adapt to changes in resource requirements and maximize the utilization rate of resources.

[0065] Refer to Figure 3 As shown, in the embodiments of the present invention, when the edge computing power sharing module executes the function of performing resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain resource nodes, it specifically is used for: S31. Generate a matching weight according to the resource demand data; S32. Perform data screening on the multi-layer dynamic resource pool table according to the division levels of the multi-layer dynamic resource pool table and the resource demand data to obtain candidate sub-pools; S33. Perform weighted calculation using the matching weight and the candidate sub-pools to obtain a weight score; S34. Screen out resource nodes from the candidate sub-pools according to the weight score.

[0066] In the embodiments of the present invention, the matching weight constructs a weight model based on resource demand characteristics (such as compute-intensive, low latency requirements), uses the analytic hierarchy process to quantify the priorities of each demand dimension (CPU / storage / network), dynamically adjusts the weight coefficient in combination with the historical scheduling success rate, generates a 0-1 standardized multi-dimensional weight vector, and provides a quantitative basis for subsequent matching.

[0067] In the embodiments of the present invention, the data screening is filtered layer by layer according to the resource pool level rules (geographical layer / service layer): first match the physically adjacent layer, and then converge the scope according to the service type. Use a bitmap index to quickly exclude resources that do not meet the hard constraints (such as inconsistent architecture types), and accelerate the query in combination with a Bloom filter, and output a resource subset that meets the basic conditions.

[0068] In the embodiments of the present invention, the weighted calculation performs a dot product operation on the attribute matrix of the candidate resources and the weight vector, and superimposes a health decay factor (such as when the health score <0.6, the score × 0.5). Introduce entropy value correction to avoid weight polarization, and standardize the score to a percentage system through Z-Score to generate a weight score with a confidence interval.

[0069] In the embodiments of the present invention, the resource node screening is to sort the candidate nodes in descending order of score and implement diversity control: select the TopN nodes (N = number of demand replicas × 3), exclude redundant nodes in the same failure domain based on the anti-affinity strategy, and finally output the optimal node set, while retaining the sub-optimal nodes as elastic alternatives.

[0070] In the embodiments of the present invention, through the extraction of idle resources and the precise matching of resource requirements, resource allocation is optimized, resource waste is reduced, and the utilization efficiency of resources is improved. This dynamic adjustment ability also enhances the flexibility and response speed of the system.

[0071] The charging power distribution module 104 is configured to perform data distribution to resource nodes according to a resource sharing network to obtain distributed data, use the resource nodes to calculate the charging pile power for the distributed data to obtain the allocated power, and perform power distribution to the charging piles according to the allocated power to obtain the charging pile power distribution result.

[0072] In the embodiment of the present invention, based on the resource sharing network, this module can perform power distribution to ensure that each charging pile obtains the required charging power. With the support of resource nodes, the power requirements of the charging piles are accurately calculated and distributed, avoiding excessive consumption of power resources.

[0073] In the embodiment of the present invention, when the charging power distribution module executes the function of performing data distribution to resource nodes according to the resource sharing network to obtain distributed data, it is specifically configured to: Generate a distribution path priority list according to the resource sharing network; Perform data encapsulation on the fusion data corresponding to the resource demand data to obtain a standardized data packet; Push the standardized data packet to the resource nodes based on the distribution path priority list to obtain the distributed data.

[0074] In the embodiment of the present invention, based on the graph structure of the resource sharing network, the improved Dijkstra algorithm is used to calculate the shortest paths 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 rate).

[0075] In the embodiment of the present invention, the resource demand data is processed by layer according to the protocol stack: metadata (timestamp, traceability hash) is injected at the application layer, the SM4 national encryption algorithm is used at the encryption layer, the DEFLATE algorithm is used at the compression layer to reduce the volume, and the QUIC packet header is encapsulated at the transport layer to generate a lightweight and anti-interference standardized data packet.

[0076] In the embodiment of the present invention, when the standardized data packet is successfully pushed to the resource nodes and is processed or directly used as input for subsequent processes (such as calculating the allocated power), this part of the data becomes the distributed data.

[0077] In the embodiment of the present invention, by optimizing the data distribution path, the data transmission time and delay are reduced, thereby improving the overall operation efficiency of the system.

[0078] In the embodiment of the present invention, when the charging power distribution module executes the function of using the resource nodes to calculate the charging pile power for the distributed data to obtain the allocated power, it is specifically configured to: Use the public key corresponding to the resource node in the distributed data to decrypt the digital signature in the distributed data to obtain the decryption result; Perform data verification on the decryption result, perform data parsing on the distributed data that passes the data verification, and obtain the parsed data; Calculate the allocated power according to the preset grid dynamic attenuation coefficient and the parsed data.

[0079] In the embodiment of the present invention, an asymmetric encryption system is adopted, and the digital signature in the distributed data is decrypted by the public key preset in the resource node to obtain the decryption result.

[0080] In the embodiment of the present invention, the following formula is used to calculate the allocated power:

[0081] Where, is the allocated power corresponding to the th charging pile, is the preset grid dynamic attenuation coefficient, is the required power corresponding to the th charging pile in the parsed data, is the load value of the grid real-time monitoring in the parsed data, is the upper limit load value of the grid safe operation in the parsed data, is the total power that the grid can currently provide in the parsed data.

[0082] In the embodiment of the present invention, the is an adjustment factor to achieve dynamic load adjustment, incorporate the grid load into the allocation logic, high load , indicating enhanced attenuation and reduced allocated power to prevent overload, low load , indicating weakened attenuation and maximizing on-demand allocation.

[0083] In the embodiment of the present invention, the restored hash value is compared with the hash value of the current data (i.e., the content of the decrypted distributed data) calculated by the receiving end according to the same algorithm. If the two match, it indicates that the data has not been modified during the transmission process and indeed comes from the sender with the corresponding private key. Once the data verification is successful, the next step is to convert it from the transmission format (such as an encrypted message, an encoded string, etc.) to a structured format that the application can understand and use (such as a JSON object, an XML document, or other programming language-specific data structures), and extract the required parameters or variable values from the parsed data structure. For example, in the scenario of charging pile power allocation, this may include but is not limited to information such as the identifier of the charging pile, the requested charging time, the required power, etc.

[0084] In the embodiments of the present invention, by considering the required power of each charging pile and the real-time state 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 allocated to each charging pile, ensuring that while meeting the requirements of each charging pile, the utilization efficiency of the power grid is maximized.

[0085] In the embodiments of the present invention, when the charging power distribution module executes the function of performing power distribution on the charging piles according to the allocated power to obtain the charging pile power distribution result, it specifically is used for: Encapsulate the allocated power into an instruction to obtain a control instruction; Upload the control instruction to the node corresponding to the resource demand data, and the node parses the control instruction to obtain a parsed instruction; Perform power adjustment on the node corresponding to the resource demand data according to the parsed instruction to obtain the charging pile distribution result.

[0086] In the embodiments of the present invention, convert the allocated power value into an instruction executable by the device: adopt Protobuf binary encoding, add the target node ID, the effective time window (start and end timestamps), and the execution priority label. Embed the CRC32 check code in the instruction header, encrypt sensitive parameters using the national cipher SM4 algorithm, and generate a standard control instruction packet with a digital signature to obtain the control instruction.

[0087] In the embodiments of the present invention, push it to the target node through the Internet of Things protocol (such as MQTT, QoS2 protocol), and deploy an instruction parsing engine on the node side: first verify the validity of the digital signature and the timestamp, decrypt it to obtain the parsed instruction, and trigger the retransmission mechanism when an abnormal instruction (check failure / timeout) is obtained.

[0088] In the embodiments of the present invention, the node power controller receives the parsed instruction, adjusts the output of the IGBT module in a closed loop through the PID algorithm, monitors the voltage / current changes in real time and feeds back to adjust the deviation (error < ±1%). Adopt dual PWM signal redundant output, and the main and standby channels switch in milliseconds. Finally, generate a power distribution result log with a timestamp and synchronously update it to the resource pool status table.

[0089] Further, the power controller in the node receives the parsed instruction and performs closed-loop regulation on the output of the IGBT (Insulated Gate Bipolar Transistor) module using the PID (Proportional-Integral-Derivative) algorithm. The PID algorithm monitors the changes in voltage and current in real time and adjusts the deviation accordingly to ensure that the output power precisely meets the requirements. The error is controlled within ±1% during this process, ensuring high-precision power adjustment.

[0090] In the embodiments of the present invention, through precise power distribution, the situation of insufficient power or waste of the charging pile is avoided, the utilization rate of electric power resources is improved, and the charging efficiency of the charging pile and the satisfaction of users are increased.

[0091] As shown Figure 4 in the figure, it is a schematic flowchart of a charging pile control method based on the Internet of Things provided by an embodiment of the present invention. In the embodiment of the present invention, the charging pile control method based on the Internet of Things includes: 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 identifier according to the charging pile identification data, and perform digital binding on the charging pile hardware identifier based on an asymmetric encryption distributed ledger to obtain a unique charging pile identifier; S402. Use the unique charging pile identifier to perform data identification on the charging pile operation data to obtain fusion data, and construct a resource sharing network based on the Internet of Things technology and the fusion data; S403. Extract idle resources in 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 according to the idle resource data, and perform resource matching on the resource demand data and the multi-layer dynamic resource pool table to obtain resource nodes; S404. Perform data distribution on the resource nodes according to the resource sharing network to obtain distribution data, use the resource nodes to calculate the charging pile power for the distribution data to obtain the allocated power, and perform power distribution on the charging pile according to the allocated power to obtain the charging pile power distribution result.

[0092] Embodiments of the present application can acquire and process relevant data based on artificial intelligence technology.

[0093] Among them, artificial intelligence (AI) is a 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.

[0094] In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in the system can also be implemented by one unit or system through software or hardware. Words such as first and second are used to represent names and do not represent any specific order.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced 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 allocation module. Specifically: The charging pile authentication module is used to obtain the charging pile data, split the charging pile data into charging pile operation data and charging pile identification data, generate the charging pile hardware identification according to 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; 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, performs resource demand analysis on the resource sharing network, obtains resource demand data, builds 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; The charging power allocation module is used to distribute data to resource nodes according to 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 pile according to the allocated power to obtain the charging pile power allocation result.

2. A charging pile control system based on the Internet of Things as claimed in claim 1, characterized in that: When the charging pile authentication module performs the function of generating the charging pile hardware identification according to the charging pile identification data, it is specifically used to: Normalizing the charging pile identification data to obtain normalized data; Performing hierarchical hash calculation on the normalized data to obtain hierarchical data; Perform XOR fusion on the layered data to obtain the charging pile hardware identification.

3. A charging pile control system based on the Internet of Things as claimed in claim 1, characterized in that: When the charging pile authentication module performs the function of digitally binding the charging pile hardware identification by the distributed ledger based on asymmetric encryption to obtain the unique identification of the charging pile, it is specifically used to: Digitally sign the charging pile hardware identifier using the private key in the 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 of the preset distributed ledger network to obtain the updated ledger; Using the public key in the charging pile key pair, the updated account book and the tangle transaction to verify the legitimacy of the charging pile hardware identifier, and obtain a verification result; According to the verification results, legal transactions are filtered out from the tangle transactions, the hash value of the legal transactions is calculated, the tangle hash is obtained, and the tangle hash is used as the unique identifier of the charging pile.

4. A charging pile control system based on the Internet of Things as claimed in claim 3, characterized in that: When the resource network construction module performs the function of using the unique identification of the charging pile to identify the charging pile operation data and obtain the fused data, it is specifically used to: Arrange the charging pile operation data in time and space to obtain arrangement data; Map the arranged data to the demand and 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.

5. A 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 the Internet of Things technology and fusion 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; Dynamically calculate the weight of Unicom data based on resource health and fusion data to obtain weight data; The network topology is constructed according to the weight data and the connection data to obtain a resource sharing network.

6. 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 multi-layer dynamic resource pool table according to the 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 according to 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.

7. A charging pile control system based on the Internet of Things as claimed in claim 6, characterized in that: 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: Generate matching weights based on resource demand data; Screening the multi-layer dynamic resource pool table according to the division levels and resource demand data of the multi-layer dynamic resource pool table to obtain candidate sub-pools; Use the matching weight and candidate sub-pool for weighted calculation to obtain a weighted score; Resource nodes are selected from the candidate sub-pool based on the weight score.

8. The charging pile control system based on the Internet of Things as claimed in claim 1, characterized in that: 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 the distribution data, it is specifically used to: generating a distribution path priority list according to the resource sharing network; Encapsulate the fused data corresponding to the resource demand data to obtain a standardized data packet; The standardized data packets are pushed to the resource nodes based on the distribution path priority list to obtain the distribution data.

9. The charging pile control system based on the Internet of Things as claimed in claim 1, characterized in that: When the charging power allocation module performs the function of calculating the charging pile power of the distributed data using the resource node to obtain the allocated power, it is specifically used to: Decrypt the digital signature in the distributed data using the public key corresponding to the resource node in the distributed data to obtain the decryption result; Perform data verification on the decryption result, and perform data parsing on the distribution data that has passed the data verification to obtain parsed data; The distributed power is calculated based on the preset grid dynamic attenuation coefficient and analytical data.

10. 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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