Charging pile equipment data center platform based on edge calculation

Abstract

The invention discloses a charging pile equipment data center platform based on edge calculation. Comprising an object model definition module, an edge gateway module, a protocol coding and decoding module, a northbound interface service module and a background monitoring module. The physical model definition module standardizes charging pile equipment data; the edge gateway module is elastically deployed to realize data interaction; the protocol encoding and decoding module provides a corresponding SDK file to realize data format conversion; the northbound interface service module provides a standardized data subscription interface for the charging operation platform; and the background monitoring module realizes intelligent monitoring. The processing method comprises data definition, edge computing node deployment, data compression transmission, subscription data pushing and system monitoring adjustment. According to the invention, equipment protocols of various manufacturer models can be accessed according to the physical model standard, northbound interface services are provided for an upper-layer charging operation system through unified data, the charging equipment protocol access cost of an upper-layer application is reduced, and the centralized management requirement of equipment data cloud is met.

Description

Technical Field

[0001] This invention relates to the field of data transmission and management technology, and specifically to a data center platform for charging pile equipment based on edge computing. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the construction scale of charging piles is constantly expanding, and their number is increasing day by day. However, the current charging pile market has many manufacturers and different models of equipment. The charging pile equipment of various manufacturers has significant differences in communication protocols, data formats, etc., which brings many challenges to the charging operation system.

[0003] Firstly, regarding device protocol access, if the charging operation system needs to communicate with charging pile devices from different manufacturers and of different models, it requires developing a dedicated access module for each device protocol. This not only increases development costs and time but also makes system maintenance and upgrades extremely complex. Once a new charging pile device model or protocol emerges, the operation system needs to be redeveloped for compatibility, which greatly limits the scalability and flexibility of the charging operation system.

[0004] Secondly, regarding equipment data management, the inconsistent data formats of various charging pile devices make it difficult for the charging operation system to centrally manage and analyze this data. The representation and meaning of telemetry data, attribute data, and event data from different devices vary, requiring the operation system to expend significant effort to process and convert this data in order to extract valuable information. This results in low data processing efficiency and an inability to provide timely and accurate data support for operational decisions.

[0005] Furthermore, with the continuous increase in the number of charging piles, the reliability and efficiency of data transmission have become a critical issue. Traditional data transmission methods cannot meet the needs of large-scale charging pile equipment data uploading to the cloud, and are prone to problems such as data loss and delays, affecting the normal operation of the charging operation system.

[0006] Therefore, there is an urgent need for a technical solution that can address the cost issues of charging equipment protocol access and the need for centralized cloud management of equipment data, in order to improve the efficiency and reliability of charging operation systems and promote the healthy development of the new energy vehicle charging industry. Summary of the Invention

[0007] To address the aforementioned issues, this invention provides a charging pile equipment data center platform based on edge computing. This platform integrates the protocols of various manufacturers and models according to a physical model standard, providing a unified data flow and a northbound interface service to the upper-layer charging operation system. Upper-layer applications only need to subscribe to the required equipment and data standards (telemetry, attributes, events). This solves the cost problem of charging equipment protocol access and the centralized management requirement for cloud-based equipment data for upper-layer applications.

[0008] The objective of this invention is achieved through the following technical solution: Firstly, an edge computing-based data center platform for charging pile equipment is provided, comprising: The object model definition module is configured to acquire telemetry data, attribute data, and event data of each charging pile and generate standardized object model data; The edge gateway module is configured as an edge gateway that can be flexibly deployed on charging piles. It creates a first protocol and a second protocol through a virtualization protocol, interacts with the charging pile through the local intranet, and interacts with the data center platform through the external network to exchange standardized data on the object model. The protocol encoding / decoding module is configured to provide SDK files corresponding to different charging pile protocols, and to encode the device uplink data into standardized data according to the protocol type object model; The northbound interface service module is configured to obtain the device status of the charging pile, the resource status of the edge gateway, and the data push concurrency at a preset sampling period, and to generate abnormal alarms by setting a preset threshold.

[0009] As a preferred embodiment, the protocol encoding / decoding module includes: The protocol encoding and decoding unit is configured to receive uplink data packets from each charging pile, strip the virtualization protocol header from the data packets received through the first protocol channel, extract the original device identifier, and then match the corresponding SDK file. The SDK management unit is configured to generate cross-regional protocol conversion middleware for devices accessing the second protocol channel. The middleware includes an SDK installation package containing a protocol parsing library, dependent components, and test cases.

[0010] As a preferred approach, the edge gateway module performs its resilient deployment by including the following steps: Create an independent process corresponding to each of the aforementioned charging piles, and run the private communication service of each charging pile manufacturer within a proprietary protocol container; Create a virtualization protocol that includes a first protocol and a second protocol; the first protocol is adapted to charging piles of different service providers within the same geographical area, and the second protocol is adapted to charging piles of the same service provider across geographical areas. Through load balancing settings, data processing resources are allocated according to protocol type. For the first protocol data, edge gateways are dynamically allocated to perform calculations based on device density. For the second protocol data, transmission priority queues are set according to geographical location. The device density is determined by counting the number of charging piles per unit area, and the geographical location priority is sorted according to the network latency between regions.

[0011] As a preferred embodiment, the northbound interface module includes: The message persistence unit is configured to distinguish the data stream identifiers of the first protocol and the second protocol, and to set up independent priority cache queues for the data in the second protocol according to their response position and response order. The flow control unit is configured to implement differentiated retry strategies based on the type of virtualization protocol, initiate fixed-interval retransmission for first protocol data when the delay exceeds a threshold, and dynamically calculate the exponential backoff time for second protocol data based on the geographical delay coefficient.

[0012] As a preferred approach, a background monitoring module is also included, which is configured to perform the following steps: Receive status data uploaded from the edge gateway module and display an online / offline distribution diagram of the charging pile equipment on the geographic information layer; Perform sliding window variance calculation on historical telemetry data to detect abnormal fluctuations in current and voltage, and match device alarm events with protocol communication failure records through correlation analysis; The historical distribution of telemetry data for each charging pile model is statistically analyzed, and the alarm threshold ranges for temperature and power are dynamically updated according to preset rules.

[0013] As a preferred embodiment, the edge gateway module includes: Data comparison unit, Configured to perform the following steps on the first protocol data: Extract dynamic fields from the standardized data of the model that are affected by power grid fluctuations, and mark the set of dynamically changing fields in the charging piles of the same power supply equipment of the first protocol at a preset proportion; Filter the data from the set of fields to identify the trend of the same field change among different service providers. If a preset number of service providers in the same region show the same field change, mark them accordingly. It is also configured to perform the following steps on the second protocol data: Extract service provider-defined extended fields, generate service provider feature hash chains, and record the continuity of field changes across regional device groups; Compare the baseline parameters of equipment from different regions of the same service provider to detect cross-regional parameter deviations. For different regions where the cross-regional parameter deviation exceeds a preset threshold, perform a retrieval of the first protocol data tag content; If the field to which the field change trend belongs is the same field as the cross-region parameter, and the change trend and deviation value of the same field are not within the preset threshold range, a data integrity alarm will be initiated. Data reconstruction unit Configured to perform the following steps on the first protocol data: Based on the region to which the charging pile belongs, merge the difference field sets of multiple edge gateways. If a preset number of adjacent nodes report the same field changes, determine the validity of the data; otherwise, perform data re-collection for the region to which the current first protocol data belongs. Compare the reconstruction of the same fields in the difference field set by different service providers through their private protocols. If the field differences between different service providers after reconstruction exceed a preset threshold, the data of most service providers whose differences are within the preset threshold shall be used as the benchmark difference field. The baseline difference field is returned and written to the first protocol data, and then reconstructed through the private protocol. Configured to perform the following steps on the second protocol data: Verify the consistency of the service provider's geographical parameters and check the continuity of the service provider's device fingerprints; If there is a pre-defined ratio of inconsistent geographical parameters of devices and inaccurate continuity of device fingerprints across regions, data reconstruction will be performed on the service provider corresponding to the second protocol data. The reconstructed object model standardized data is synchronized to each edge gateway module in the second protocol data, and the geographical area data included in the second protocol data is reconstructed through multiple different edge gateway modules to build multiple sub-second protocol data, and each of the multiple sub-second protocol data includes charging piles with overlapping geographical areas of the same service provider. The consistency of device geographic parameters and the continuity of device fingerprints for the same charging pile in different second protocol data are re-verified.

[0014] A second aspect of the present invention provides a data processing method for charging pile equipment based on edge computing, comprising the following steps: S1. Obtain telemetry data, attribute data, and event data of each charging pile, and generate standardized data for the object model; S2. Create a first protocol and a second protocol. The first protocol is adapted to charging piles from different service providers within the same geographical area, and the second protocol is adapted to charging piles from the same service provider across geographical areas. S3. Receive uplink data packets from each charging pile, strip the virtualization protocol header from the data packets passing through the first protocol channel and extract the original device identifier to match the corresponding SDK file, and generate cross-regional protocol conversion middleware for devices accessed through the second protocol channel. S3. Obtain the charging pile equipment status, edge gateway resource status and data push concurrency at a preset sampling period, and set a preset threshold to generate an abnormal alarm; S4. Perform background monitoring, including: displaying the online / offline distribution of charging piles on the geographic information layer, performing sliding window variance calculation on historical telemetry data to detect abnormal fluctuations in current and voltage and associating them with device alarms and protocol communication failure records, and statistically analyzing the historical distribution of telemetry data for each charging pile model and dynamically updating the alarm threshold ranges for temperature and power. S5. Extract dynamic fields affected by power grid fluctuations from the first protocol data, mark the field set of a preset proportion of charging piles in the same power supply equipment, filter the same field change trends of different service providers, and mark when a preset number of service providers in the same area show the same change; extract custom extended fields of service providers from the second protocol data to generate feature hash chains, record the continuity of field changes of cross-regional equipment groups, compare the baseline parameters of equipment in different regions of the same service provider and detect cross-regional parameter deviation values. If the deviation exceeds the threshold and the field is the same as the marked field of the first protocol and the change trend and deviation value exceed the preset range, then initiate a data integrity alarm. S6. Merge the difference field sets of multiple edge gateways for the first protocol data. If adjacent nodes report changes in the same field, the data validity is determined. Otherwise, data re-collection is performed. Compare the difference field sets after reconstruction of different service providers' private protocols. Use the data of most service providers and whose differences are within the threshold as the benchmark difference field and return it to be written into the first protocol data. Verify the consistency of service provider geographical parameters and the continuity of device fingerprints for the second protocol data. If there is a preset proportion of device failure criteria across regions, reconstruct the data. Synchronize the reconstructed object model data to multiple edge gateway modules and rebuild the sub-second protocol data including charging piles with overlapping geographical areas. Re-verify the geographical parameters and device fingerprints of the same charging piles.

[0015] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method of the second aspect of the present invention.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the method of the second aspect of the present invention.

[0017] The beneficial effects of this invention are as follows: This invention, through the collaborative work of the object model definition module and the protocol encoding / decoding module, enables the unified acquisition of telemetry, attribute, and event data from object model definition modules of various manufacturers and models, generating standardized data and fundamentally solving the problem of heterogeneous data formats among devices from different manufacturers. The edge gateway module is flexibly deployed on the edge gateway. Using a first and second protocol created through virtualization, it adapts to charging piles from different service providers within the same geographical area, as well as charging piles from the same service provider across regions, eliminating the need to develop separate access modules for each device protocol. For example, the first protocol can integrate the charging pile protocols of service providers A, B, and C within a region, achieving data interaction through a unified interface. When a new service provider D is added, only the SDK file needs to be updated to complete the adaptation. The SDK file provided by the protocol encoding / decoding module and the cross-regional protocol conversion middleware enable the system to quickly respond to the access of new protocol devices, greatly improving system scalability and effectively solving the problems of high development costs and complex maintenance of access modules in the background technology.

[0018] This invention ensures accurate matching of the original device identifier with the SDK file through the protocol encoding / decoding unit's processing mechanism for uplink data packets, achieving standardized data format conversion. The northbound interface service module acquires device status, edge gateway resource status, and data push concurrency at a preset sampling period. It generates anomaly alarms by setting preset thresholds, enabling the operation system to monitor device operating status in real time. The background monitoring function displays the online / offline distribution of charging piles on a geographic information layer, facilitating intuitive understanding of device deployment by management personnel. It performs sliding window variance calculation on historical telemetry data, accurately detecting abnormal fluctuations in parameters such as current and voltage. By statistically analyzing the historical distribution of telemetry data for each charging pile model and dynamically updating the alarm threshold range, alarms for parameters such as temperature and power become more accurate, solving the management and analysis difficulties caused by inconsistent data formats and providing accurate data support for operational decisions.

[0019] This invention utilizes an edge gateway module in flexible deployments to run vendor-specific communication services through independent processes, combined with load balancing to allocate data processing resources. For first-protocol data, edge gateway computing resources are dynamically allocated based on device density; as the density of charging piles per unit area increases, the system automatically increases computing resources. For second-protocol data, transmission priority queues are set according to geographical location, prioritizing data from regions with lower latency based on inter-regional network latency, thus reducing cross-regional data transmission latency. The message persistence unit distinguishes protocol data stream identifiers and sets up independent priority cache queues. The traffic control unit implements differentiated retry strategies; when the latency of first-protocol data exceeds a threshold, fixed-interval retransmission is initiated; for second-protocol data, exponential backoff time is dynamically calculated based on the geographical latency coefficient, effectively reducing data loss and solving the problems of low reliability and poor efficiency in traditional data transmission methods.

[0020] The data comparison unit of this invention extracts dynamic fields affected by power grid fluctuations from the first protocol data, and marks the field set of a preset proportion of charging piles in the same power supply equipment. When the same field changes occur in a preset number of service providers within the same area, marking is performed, which can promptly detect the impact of power grid fluctuations on the equipment. For the second protocol data, custom extended fields of service providers are extracted to generate a feature hash chain, recording the continuity of field changes across regional equipment groups. Deviation values ​​are detected by comparing the baseline parameters of equipment in different regions. If the deviation exceeds a threshold and is the same as the marked field in the first protocol, a data integrity alarm is initiated. For example, if the power parameter deviation of a service provider's cross-regional equipment exceeds a preset value and is consistent with the power field affected by power grid fluctuations in the first protocol, the system immediately issues an alarm to avoid equipment failure due to incomplete data. The data reconstruction unit merges the edge gateway difference field set of the first protocol data. If adjacent nodes report the same field changes, the data validity is determined; otherwise, data is re-collected to ensure accuracy. For the second protocol data, the consistency of geographical parameters and the continuity of equipment fingerprints are verified. If inaccuracy is found, reconstruction is performed and synchronized to the edge gateway. After re-verification, the consistency of cross-regional data is improved, further ensuring data quality.

[0021] The load balancing mechanism of the edge gateway module in this invention rationally allocates resources based on factors such as protocol type, device density, and geographical location, avoiding resource waste and overload issues. For example, in densely populated areas, the system automatically increases the allocation ratio of edge gateway computing resources to ensure data processing efficiency. The traffic control strategy of the northbound interface module, combined with the virtualization protocol type, implements differentiated retries to effectively cope with network fluctuations and ensure stable system operation in complex network environments. The dynamic threshold update mechanism of the background monitoring enables the system to adapt to the operating characteristics of different types of charging piles, reduce false alarms, improve system stability, and provide reliable technical support for large-scale charging pile deployment. Attached Figure Description

[0022] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0023] Figure 1 This is a structural block diagram of the system according to Embodiment 1 of the present invention.

[0024] Figure 2 This is a structural block diagram of the electronic device according to Embodiment 2 of the present invention.

[0025] Among them, 30 is the processor; 31 is the memory; 32 is the communication interface; and 33 is the bus. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1 like Figure 1 As shown, this disclosure provides a data center platform for charging pile equipment based on edge computing, including: The object model definition module is configured to acquire telemetry data, attribute data, and event data of each charging pile and generate standardized object model data; The edge gateway module is configured as an edge gateway that can be flexibly deployed on charging piles. It creates a first protocol and a second protocol through a virtualization protocol, interacts with the charging pile through the local intranet, and interacts with the data center platform through the external network to exchange standardized data on the object model. The protocol encoding / decoding module is configured to provide SDK files corresponding to different charging pile protocols, and to encode the device uplink data into standardized data according to the protocol type object model; The northbound interface service module is configured to obtain the device status of the charging pile, the resource status of the edge gateway, and the data push concurrency at a preset sampling period, and to generate abnormal alarms by setting a preset threshold.

[0028] As a preferred embodiment, the protocol encoding / decoding module includes: The protocol encoding and decoding unit is configured to receive uplink data packets from each charging pile, strip the virtualization protocol header from the data packets received through the first protocol channel, extract the original device identifier, and then match the corresponding SDK file. The SDK management unit is configured to generate cross-regional protocol conversion middleware for devices accessing the second protocol channel. The middleware includes an SDK installation package containing a protocol parsing library, dependent components, and test cases.

[0029] As a preferred approach, the standardized data of the object model in this embodiment is defined using JSON as multiple fields such as event, telemetry, and properties. These fields are defined by the data center platform, and the codec assigns values ​​to the corresponding fields for transmission. For example, in this embodiment, the temperature of the charging pile is the Temperature field. Furthermore, the fields within the standardized data of the object model are extensible. For instance, some charging piles have A-phase voltage and B-phase voltage, and later, when other charging piles have C-phase voltage, this is defined by the platform.

[0030] data: { "event": { "type": 1, "description": "Description" }, "telemetry": { "temperature": 27, "electricCurrent": 180, "voltage": 220 }, "properties": { "version": "1.0", "iccid": "898604112152580083222" } As a preferred approach, the edge gateway module performs its resilient deployment by including the following steps: Create an independent process corresponding to each of the aforementioned charging piles, and run the private communication service of each charging pile manufacturer within a proprietary protocol container; Create a virtualization protocol that includes a first protocol and a second protocol; the first protocol is adapted to charging piles of different service providers within the same geographical area, and the second protocol is adapted to charging piles of the same service provider across geographical areas. Through load balancing settings, data processing resources are allocated according to protocol type. For the first protocol data, edge gateways are dynamically allocated to perform calculations based on device density. For the second protocol data, transmission priority queues are set according to geographical location. The device density is determined by counting the number of charging piles per unit area, and the geographical location priority is sorted according to the network latency between regions.

[0031] As a preferred embodiment, the northbound interface module includes: The message persistence unit is configured to distinguish the data stream identifiers of the first protocol and the second protocol, and to set up independent priority cache queues for the data in the second protocol according to their response position and response order. The flow control unit is configured to implement differentiated retry strategies based on the type of virtualization protocol, initiate fixed-interval retransmission for first protocol data when the delay exceeds a threshold, and dynamically calculate the exponential backoff time for second protocol data based on the geographical delay coefficient.

[0032] As a preferred approach, a background monitoring module is also included, which is configured to perform the following steps: Receive status data uploaded from the edge gateway module and display an online / offline distribution diagram of the charging pile equipment on the geographic information layer; Perform sliding window variance calculation on historical telemetry data to detect abnormal fluctuations in current and voltage, and match device alarm events with protocol communication failure records through correlation analysis; The historical distribution of telemetry data for each charging pile model is statistically analyzed, and the alarm threshold ranges for temperature and power are dynamically updated according to preset rules.

[0033] As a preferred embodiment, the edge gateway module includes a data comparison unit and a data reconstruction unit.

[0034] The data comparison unit is configured to perform the following steps on the first protocol data: The dynamic fields affected by power grid fluctuations in the standardized data of the extract model are used to mark the set of dynamically changing fields in the charging piles of the same power supply equipment at a preset ratio in the first protocol data. Filter the data from the set of fields to identify the changing trends of the same fields among different service providers. If a preset number of service providers in the same region show changes in the same field, mark them accordingly.

[0035] The data comparison unit is also configured to perform the following steps on the second protocol data: Extract service provider-defined extended fields, generate service provider feature hash chains, and record the continuity of field changes across regional device groups; By comparing the baseline parameters of devices from different regions of the same service provider, the cross-regional parameter deviation value is detected; in this embodiment of the disclosure, the cross-regional parameter deviation is the deviation of the same parameters between charging piles from different regions of the same service provider. For different regions where the cross-regional parameter deviation exceeds a preset threshold, the tagged content of the first protocol data is retrieved; If the field to which the field change trend belongs is the same field as the cross-regional parameter, and the change trend and deviation value of the same field are not within the preset threshold range, a data integrity alarm is initiated and resampling is performed. If it is within the preset range, it is considered that the data sampled by the first protocol and the second protocol have mutually achieved data integrity calibration, and the data transmitted under the two protocol channels are all valid data.

[0036] Specifically, in this embodiment of the disclosure, the data reconstruction unit is configured to perform the following steps on the first protocol data: Based on the region to which the charging pile belongs, the difference field sets of multiple edge gateways are merged. If a preset number of adjacent nodes report the same field changes, the validity of the data is determined; otherwise, the data is re-collected for the region to which the current first protocol data belongs.

[0037] Specifically, in this embodiment of the disclosure, for the standardized data of the object model composed of different charging pile devices sampled within the jurisdiction of multiple edge gateways in the first protocol data, only the difference fields in the standardized data are extracted. These difference fields are then merged according to the JSON processing method for the standardized data in this embodiment of the disclosure to obtain a set of difference fields. Subsequently, the network relationship between each existing charging pile and its adjacent charging piles is obtained from the first protocol data. The total number of these network relationships is also positively correlated with the total number of edge gateways in the first protocol data. For example, if there is only one edge gateway in the first protocol data, then all charging piles sampled from that first edge gateway... After statistically analyzing the adjacent relationships of charging piles, multiple difference arrays of adjacent charging piles are obtained. If there are multiple edge gateways, the process of obtaining the mesh relationship and difference array is performed for each edge gateway. For charging piles with overlapping sampling coverage areas in different edge gateways, corresponding data mappings are established and written into the mesh relationship. It should be noted that the mesh relationship in this embodiment is a data storage structure. Then, within a preset fault tolerance range (i.e., the preset number of times the first protocol data is processed in this embodiment), the validity of field changes is judged. If the fault tolerance range is exceeded, it is considered that the sampling state or the charging pile state is abnormal, resulting in data inaccuracy.

[0038] The same fields in the difference field set are reconstructed by different service providers through their private protocols. If the field differences between different service providers exceed a preset threshold after reconstruction, the data of most service providers whose differences are within the preset threshold are used as the benchmark difference field. The benchmark difference field is returned and written into the first protocol data and then reconstructed through the private protocol.

[0039] The data reconstruction unit is configured to perform the following steps on the second protocol data: Verify the consistency of the service provider's geographical parameters and check the continuity of the service provider's device fingerprints; If there are a preset proportion of charging pile devices in cross-regional areas, and the geographical parameter consistency and device fingerprint continuity of these devices are inaccurate, data reconstruction is performed on the service provider corresponding to the second protocol data. In this embodiment of the disclosure, the data reconstruction involves resampling the currently inaccurate data, and then reconstructing the data into object model standardized data according to the data construction process of the second protocol data.

[0040] The reconstructed object model standardized data is synchronized to each edge gateway module in the second protocol data, and the geographical area data included in the second protocol data is reconstructed through multiple different edge gateway modules to build multiple sub-second protocol data. Each of the multiple sub-second protocol data includes charging piles from different geographical areas of the same service provider. In this embodiment, for different edge gateway modules, each edge gateway module covers charging piles in a specific area. Taking one edge gateway as an example, all data in the sampled area covered by the edge gateway is reconstructed to obtain sub-second protocol data about the second protocol data. In this embodiment, when multiple edge gateways in the same second protocol data are set, there is geographical overlap. Therefore, it is set to include charging piles from different geographical areas of the same service provider. Thus, the data in each sub-second protocol data can be compared and verified with the data of another edge gateway.

[0041] The consistency of device geographic parameters and the continuity of device fingerprints for the same charging pile in different sub-second protocol data are re-verified to determine the validity of the data. One way to determine validity is by using the threshold method. It should be noted that the threshold method is an efficient statistical method in statistics, which will not be elaborated here.

[0042] A second aspect of this disclosure provides a data processing method for charging pile equipment based on edge computing, comprising the following steps: S1. Obtain telemetry data, attribute data, and event data of each charging pile, and generate standardized data for the object model; S2. Create a first protocol and a second protocol. The first protocol is adapted to charging piles from different service providers within the same geographical area, and the second protocol is adapted to charging piles from the same service provider across geographical areas. S3. Receive uplink data packets from each charging pile, strip the virtualization protocol header from the data packets passing through the first protocol channel and extract the original device identifier to match the corresponding SDK file, and generate cross-regional protocol conversion middleware for devices accessed through the second protocol channel. S3. Obtain the charging pile equipment status, edge gateway resource status and data push concurrency at a preset sampling period, and set a preset threshold to generate an abnormal alarm; S4. Perform background monitoring, including: displaying the online / offline distribution of charging piles on the geographic information layer, performing sliding window variance calculation on historical telemetry data to detect abnormal fluctuations in current and voltage and associating them with device alarms and protocol communication failure records, and statistically analyzing the historical distribution of telemetry data for each charging pile model and dynamically updating the alarm threshold ranges for temperature and power. S5. Extract dynamic fields affected by power grid fluctuations from the first protocol data, mark the field set of a preset proportion of charging piles in the same power supply equipment, filter the same field change trends of different service providers, and mark when a preset number of service providers in the same area show the same change; extract custom extended fields of service providers from the second protocol data to generate feature hash chains, record the continuity of field changes of cross-regional equipment groups, compare the baseline parameters of equipment in different regions of the same service provider and detect cross-regional parameter deviation values. If the deviation exceeds the threshold and the field is the same as the marked field of the first protocol and the change trend and deviation value exceed the preset range, then initiate a data integrity alarm. S6. Merge the difference field sets of multiple edge gateways for the first protocol data. If adjacent nodes report changes in the same field, the data validity is determined. Otherwise, data re-collection is performed. Compare the difference field sets after reconstruction of different service providers' private protocols. Use the data of most service providers and whose differences are within the threshold as the benchmark difference field and return it to be written into the first protocol data. Verify the consistency of service provider geographical parameters and the continuity of device fingerprints for the second protocol data. If there is a preset proportion of device failure criteria across regions, reconstruct the data. Synchronize the reconstructed object model data to multiple edge gateway modules and rebuild the sub-second protocol data including charging piles with overlapping geographical areas. Re-verify the geographical parameters and device fingerprints of the same charging piles.

[0043] The beneficial effects of the embodiments disclosed herein are as follows: This embodiment of the disclosure, through the collaborative work of the object model definition module, protocol encoding and decoding module, etc., can uniformly acquire telemetry, attribute, and event data from object model definition modules of various manufacturers and generate standardized data, thus solving the problem of heterogeneous data formats of devices from different manufacturers from the source. The edge gateway module is flexibly deployed on the edge gateway. Using a first protocol and a second protocol created with virtualization protocols, it adapts to charging piles from different service providers in the same geographical area and charging piles from the same service provider across regions, respectively, without requiring separate development of access modules for each device protocol. For example, the first protocol can integrate the charging pile protocols of three service providers A, B, and C in the region, achieving data interaction through a unified interface. When a new service provider D is added, only the SDK file needs to be updated to complete the adaptation. The SDK file provided by the protocol encoding and decoding module and the cross-regional protocol conversion middleware enable the system to quickly respond to the access of new protocol devices, greatly improving system scalability and effectively solving the problems of high development costs and complex maintenance of access modules in the background art.

[0044] This embodiment of the disclosure ensures accurate matching of the original device identifier with the SDK file through the protocol encoding and decoding unit's processing mechanism for uplink data packets, achieving standardized data format conversion. The northbound interface service module acquires device status, edge gateway resource status, and data push concurrency at a preset sampling period, generating abnormal alarms by setting preset thresholds, enabling the operation system to monitor device operating status in real time. The background monitoring function displays the online / offline distribution of charging piles on a geographic information layer, facilitating managers' intuitive understanding of device deployment; it performs sliding window variance calculation on historical telemetry data, accurately detecting abnormal fluctuations in parameters such as current and voltage. By statistically analyzing the historical distribution of telemetry data for each charging pile model and dynamically updating the alarm threshold range, alarms for parameters such as temperature and power become more accurate, solving the management and analysis difficulties caused by inconsistent data formats and providing accurate data support for operational decisions.

[0045] This embodiment utilizes an edge gateway module in a flexible deployment scenario. It creates an independent process to run the vendor's proprietary communication service and allocates data processing resources using load balancing. For the first protocol data, edge gateway computing resources are dynamically allocated based on device density. As the density of charging piles per unit area increases, the system automatically increases computing resources. For the second protocol data, a transmission priority queue is set up based on geographical location. Data from regions with lower latency is prioritized for transmission, reducing cross-regional data transmission latency. The message persistence unit distinguishes protocol data stream identifiers and sets up independent priority cache queues. The traffic control unit implements a differentiated retry strategy. When the latency of the first protocol data exceeds a threshold, a fixed-interval retransmission is initiated. For the second protocol data, the exponential backoff time is dynamically calculated based on the geographical latency coefficient, effectively reducing data loss and solving the problems of low reliability and poor efficiency in traditional data transmission methods.

[0046] The data comparison unit of this embodiment extracts dynamic fields affected by power grid fluctuations from the first protocol data and marks a set of fields for a preset proportion of charging piles in the same power supply equipment. When a preset number of service providers in the same area show the same field change, the marking is executed, which can promptly detect the impact of power grid fluctuations on the equipment. For the second protocol data, custom extended fields of the service providers are extracted to generate a feature hash chain, recording the continuity of field changes across regional equipment groups. The deviation values ​​are compared with the baseline parameters of equipment in different regions. If the deviation exceeds a threshold and is the same as the marked field in the first protocol, a data integrity alarm is initiated. For example, if the power parameter deviation of a service provider's cross-regional equipment exceeds a preset value and is consistent with the power field affected by power grid fluctuations in the first protocol, the system immediately alarms to avoid equipment failure due to incomplete data. The data reconstruction unit merges the edge gateway difference field set of the first protocol data. If adjacent nodes report the same field change, the data validity is determined; otherwise, data is re-collected to ensure data accuracy. The second protocol data is verified for geographical parameter consistency and equipment fingerprint continuity. If inaccuracy is found, reconstruction is performed and synchronized to the edge gateway. After re-verification, cross-regional data consistency is improved, further ensuring data quality.

[0047] The load balancing mechanism of the edge gateway module in this disclosure rationally allocates resources based on factors such as protocol type, device density, and geographical location, avoiding resource waste and overload issues. For example, in densely populated areas, the system automatically increases the allocation ratio of edge gateway computing resources to ensure data processing efficiency. The traffic control strategy of the northbound interface module, combined with the virtualization protocol type, implements differentiated retries to effectively cope with network fluctuations and ensure stable system operation in complex network environments. The dynamic threshold update mechanism of the background monitoring enables the system to adapt to the operating characteristics of different types of charging piles, reduce false alarms, improve system stability, and provide reliable technical support for large-scale charging pile deployment.

[0048] Example 2 Combination Figure 2 As shown, this embodiment of the disclosure provides an electronic device, including a processor 30 and a memory 31. Optionally, the electronic device may further include a communication interface 32 and a bus 33. The processor 30, communication interface 32, and memory 31 can communicate with each other via the bus 33. The communication interface 32 can be used for information transmission. The processor 30 can invoke logical instructions in the memory 31 to execute the method described in Embodiment 1.

[0049] This disclosure provides a storage medium storing computer-executable instructions configured to perform the method as described in Embodiment 1.

[0050] The aforementioned storage media can be either transient computer-readable storage media or non-transitory computer-readable storage media. Non-transitory storage media include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, and can also be transient storage media.

[0051] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0052] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to achieve the described functions, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described devices, apparatuses, and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0053] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, function, and operation of possible implementations of apparatus, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than those disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A data center platform for charging pile equipment based on edge computing, characterized in that, include: The object model definition module is configured to acquire telemetry data, attribute data, and event data of each charging pile and generate standardized object model data; The edge gateway module is configured as an edge gateway that can be flexibly deployed on charging piles. It creates a first protocol and a second protocol through a virtualization protocol, interacts with the charging pile through the local intranet, and interacts with the data center platform through the external network to exchange standardized data on the object model. The protocol encoding / decoding module is configured to provide SDK files corresponding to different charging pile protocols, and to encode the device uplink data into standardized data according to the protocol type object model; The northbound interface service module is configured to obtain the device status of the charging pile, the resource status of the edge gateway, and the data push concurrency at a preset sampling period, and to generate abnormal alarms by setting a preset threshold.

2. The edge computing-based charging pile equipment data center platform according to claim 1, characterized in that, The protocol encoding / decoding module includes: The protocol encoding and decoding unit is configured to receive uplink data packets from each charging pile, strip the virtualization protocol header from the data packets received through the first protocol channel, extract the original device identifier, and then match the corresponding SDK file. The SDK management unit is configured to generate cross-regional protocol conversion middleware for devices accessing the second protocol channel. The middleware includes an SDK installation package containing a protocol parsing library, dependent components, and test cases.

3. The charging pile equipment data center platform according to claim 1, characterized in that, When the edge gateway module performs its elastic deployment, it includes the following steps: Create an independent process corresponding to each of the aforementioned charging piles, and run the private communication service of each charging pile manufacturer within a proprietary protocol container; Create a virtualization protocol that includes a first protocol and a second protocol; the first protocol is adapted to charging piles of different service providers within the same geographical area, and the second protocol is adapted to charging piles of the same service provider across geographical areas. Through load balancing settings, data processing resources are allocated according to protocol type. For the first protocol data, edge gateways are dynamically allocated to perform calculations based on device density. For the second protocol data, transmission priority queues are set according to geographical location. The device density is determined by counting the number of charging piles per unit area, and the geographical location priority is sorted according to the network latency between regions.

4. The data center platform for charging pile equipment based on edge computing according to claim 1, characterized in that, The northbound interface module includes: The message persistence unit is configured to distinguish the data stream identifiers of the first protocol and the second protocol, and to set up independent priority cache queues for the data in the second protocol according to their response position and response order. The flow control unit is configured to implement differentiated retry strategies based on the type of virtualization protocol, initiate fixed-interval retransmission for first protocol data when the delay exceeds a threshold, and dynamically calculate the exponential backoff time for second protocol data based on the geographical delay coefficient.

5. The edge computing-based charging pile equipment data center platform according to claim 1, characterized in that, It also includes a backend monitoring module, which is configured to perform the following steps: Receive status data uploaded from the edge gateway module and display an online / offline distribution diagram of the charging pile equipment on the geographic information layer; Perform sliding window variance calculation on historical telemetry data to detect abnormal fluctuations in current and voltage, and match device alarm events with protocol communication failure records through correlation analysis; The historical distribution of telemetry data for each charging pile model is statistically analyzed, and the alarm threshold ranges for temperature and power are dynamically updated according to preset rules.

6. The edge computing-based charging pile equipment data center platform according to claim 3, characterized in that, The edge gateway module includes: Data comparison unit, Configured to perform the following steps on the first protocol data: Extract dynamic fields from the standardized data of the model that are affected by power grid fluctuations, and mark the set of dynamically changing fields in the charging piles of the same power supply equipment of the first protocol at a preset proportion; Filter the data from the set of fields to identify the trend of the same field change among different service providers. If a preset number of service providers in the same region show the same field change, mark them accordingly. It is also configured to perform the following steps on the second protocol data: Extract service provider-defined extended fields, generate service provider feature hash chains, and record the continuity of field changes across regional device groups; Compare the baseline parameters of equipment from different regions of the same service provider to detect cross-regional parameter deviations. For different regions where the cross-regional parameter deviation exceeds a preset threshold, perform a retrieval of the first protocol data tag content; If the field to which the field change trend belongs is the same field as the cross-region parameter, and the change trend and deviation value of the same field are not within the preset threshold range, a data integrity alarm will be initiated. Data reconstruction unit Configured to perform the following steps on the first protocol data: Based on the region to which the charging pile belongs, merge the difference field sets of multiple edge gateways. If a preset number of adjacent nodes report the same field changes, determine the validity of the data; otherwise, perform data re-collection for the region to which the current first protocol data belongs. Compare the reconstruction of the same fields in the difference field set by different service providers through their private protocols. If the field differences between different service providers after reconstruction exceed a preset threshold, the data of most service providers whose differences are within the preset threshold shall be used as the benchmark difference field. The baseline difference field is returned and written to the first protocol data, and then reconstructed through the private protocol. Configured to perform the following steps on the second protocol data: Verify the consistency of the service provider's geographical parameters and check the continuity of the service provider's device fingerprints; If there is a pre-defined ratio of inconsistent geographical parameters of devices and inaccurate continuity of device fingerprints across regions, data reconstruction will be performed on the service provider corresponding to the second protocol data. The reconstructed object model standardized data is synchronized to each edge gateway module in the second protocol data, and the geographical area data included in the second protocol data is reconstructed through multiple different edge gateway modules to build multiple sub-second protocol data, and each of the multiple sub-second protocol data includes charging piles of the same service provider in different geographical areas. The consistency of device geographic parameters and the continuity of device fingerprints for the same charging pile in different sub-second protocol data are re-verified.

7. A data processing method for charging pile equipment based on edge computing, characterized in that, Includes the following steps: S1. Obtain telemetry data, attribute data, and event data of each charging pile, and generate standardized data for the object model; S2. Create a first protocol and a second protocol. The first protocol is adapted to charging piles from different service providers within the same geographical area, and the second protocol is adapted to charging piles from the same service provider across geographical areas. S3. Receive uplink data packets from each charging pile, strip the virtualization protocol header from the data packets passing through the first protocol channel and extract the original device identifier to match the corresponding SDK file, and generate cross-regional protocol conversion middleware for devices accessed through the second protocol channel. S3. Obtain the charging pile equipment status, edge gateway resource status and data push concurrency at a preset sampling period, and set a preset threshold to generate an abnormal alarm; S4. Perform background monitoring, including: displaying the online / offline distribution of charging piles on the geographic information layer, performing sliding window variance calculation on historical telemetry data to detect abnormal fluctuations in current and voltage and associating them with device alarms and protocol communication failure records, and statistically analyzing the historical distribution of telemetry data for each charging pile model and dynamically updating the alarm threshold ranges for temperature and power. S5. Extract dynamic fields affected by power grid fluctuations from the first protocol data, mark the field set of a preset proportion of charging piles in the same power supply equipment, filter the same field change trends of different service providers, and mark when a preset number of service providers in the same area show the same change; extract custom extended fields of service providers from the second protocol data to generate feature hash chains, record the continuity of field changes of cross-regional equipment groups, compare the baseline parameters of equipment in different regions of the same service provider and detect cross-regional parameter deviation values. If the deviation exceeds the threshold and the field is the same as the marked field of the first protocol and the change trend and deviation value exceed the preset range, then initiate a data integrity alarm. S6. Merge the difference field sets of multiple edge gateways for the first protocol data. If adjacent nodes report changes in the same field, the data validity is determined. Otherwise, data re-collection is performed. Compare the difference field sets after reconstruction of different service providers' private protocols. Use the data of most service providers and whose differences are within the threshold as the benchmark difference field and return it to be written into the first protocol data. Verify the consistency of service provider geographical parameters and the continuity of device fingerprints for the second protocol data. If there is a preset proportion of device failure criteria across regions, reconstruct the data. Synchronize the reconstructed object model data to multiple edge gateway modules and rebuild the sub-second protocol data including charging piles with overlapping geographical areas. Re-verify the geographical parameters and device fingerprints of the same charging piles.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method as described in claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in claim 7.

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