Adaptive configuration method and system for energy storage system communication interface

Abstract

The application discloses a kind of self-adapting configuration method and system of energy storage system communication interface, including obtaining the communication protocol data information of energy storage equipment to be accessed;Protocol characteristic vector of the energy storage equipment to be accessed is calculated;Initial interface configuration parameter of the energy storage equipment to be accessed is generated;Integrity, timing and safety compliance verification are carried out to initial interface configuration parameter, and the interface configuration parameter of the energy storage equipment to be accessed is obtained;According to the interface configuration parameter obtained, the update of protocol rule library is carried out, and the self-adapting configuration of the communication interface of the energy storage equipment to be accessed is completed.The application not only realizes the self-adapting configuration of energy storage system communication interface, but also has higher reliability, better accuracy and higher efficiency.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, and specifically relates to an adaptive configuration method and system for the communication interface of an energy storage system. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, as more and more new energy power generation systems are integrated into the power grid and generate electricity, the randomness and intermittency of their output pose significant challenges to the safe and stable operation of the power system. Therefore, power systems are gradually beginning to incorporate energy storage systems to improve their own safe and stable operation capabilities, while also enhancing the absorption capacity of new energy sources.

[0004] However, in the actual operation of energy storage systems, different manufacturers, models, and versions of energy storage devices often use different communication protocols. These protocols differ significantly in data format, timing logic, and security mechanisms. If the communication interface configurations of these devices are mismatched during collaborative operation, it will directly lead to data exchange failure between the devices. Furthermore, as the scale of energy storage sites continues to expand and protocol versions are frequently updated, the configuration of communication interfaces between different devices in the energy storage system will become increasingly complex.

[0005] Currently, the commonly used communication interface configuration scheme for energy storage systems often relies on manual configuration by energy storage system personnel. However, this approach is not only time-consuming and labor-intensive, but also inefficient, and its reliability and accuracy cannot be guaranteed. Summary of the Invention

[0006] One of the objectives of this invention is to provide an adaptive configuration method for the communication interface of an energy storage system that is highly reliable, accurate, and efficient.

[0007] The second objective of this invention is to provide a system for implementing an adaptive configuration method for the communication interface of the energy storage system.

[0008] The adaptive configuration method for the communication interface of an energy storage system provided by this invention includes the following steps:

[0009] S1. Obtain the communication protocol data information of the energy storage device to be connected;

[0010] S2. Based on the data information obtained in step S1, the protocol feature vector of the energy storage device to be connected is calculated according to the coding entropy scheme, communication time threshold and encryption scheme data.

[0011] S3. Based on the protocol feature vector obtained in step S2, generate the initial interface configuration parameters of the energy storage device to be connected based on the pre-trained deep neural network model;

[0012] S4. Verify the integrity, timing, and security compliance of the initial interface configuration parameters generated in step S3 to obtain the interface configuration parameters of the energy storage device to be connected;

[0013] S5. Based on the interface configuration parameters obtained in step S4, update the protocol rule base according to the incremental update scheme, and complete the adaptive configuration of the communication interface of the energy storage device to be connected.

[0014] Step S1 specifically includes the following steps:

[0015] Obtain communication protocol data information of the energy storage device to be connected;

[0016] The communication protocol data information includes data format characteristics, timing logic characteristics, and security mechanism characteristics;

[0017] The data format features include the number of data bits, the type of check bit, the byte order, and the encoding rules; the timing logic features include the communication frame interval time; and the security mechanism features include the encryption algorithm type, the authentication protocol version, and the access control policy.

[0018] Step S2 includes the following steps:

[0019] The coding entropy formula is used to calculate the coding entropy of the energy storage device to be connected.

[0020] Based on statistical data of communication frame interval time, the stability coefficient of the energy storage device to be connected is calculated.

[0021] Based on the encryption algorithm type, authentication protocol version, and access control policy, the comprehensive security strength coefficient of the energy storage device to be connected is calculated.

[0022] The obtained encoding entropy, stability coefficient, and comprehensive security mechanism strength coefficient are used to construct the protocol feature vector of the energy storage device to be connected.

[0023] Step S2 specifically includes the following steps:

[0024] The coding entropy is calculated using the following formula. :

[0025]

[0026] In the formula Let be the probability of the i-th type of coded symbol appearing during the communication process; n is the number of types of coded symbols.

[0027] The stability coefficient is calculated using the following formula. :

[0028]

[0029] In the formula The standard deviation of the communication frame interval time; This represents the average time interval between communication frames;

[0030] The comprehensive strength coefficient of the safety mechanism is calculated using the following formula. :

[0031]

[0032] In the formula Weighting coefficients for encryption algorithm types; This is a quantization value for the strength of the encryption algorithm. In practice, it can be assigned a value from 0 to 1 according to the security level of the encryption algorithm. The weighting factor for the authentication protocol version; The security quantification value for the set authentication protocol version can be assigned from 0 to 1 based on the security and compatibility of the protocol during specific implementation. The weighting coefficients for access control policies; This is a quantification value for the strictness of the access control policy. In practice, it can be assigned a value from 0 to 1 according to the granularity of the policy.

[0033] Encode entropy Stability coefficient Comprehensive strength coefficient of security mechanism The protocol feature vector of the energy storage device to be connected is constructed. for .

[0034] Step S3 specifically includes the following steps:

[0035] A pre-trained deep neural network based on attention weights is used to process the protocol feature vector obtained in step S2 to generate the initial interface configuration parameters of the energy storage device to be connected.

[0036] When training a deep neural network based on attention weights, the following formula is used as the loss function for the training process:

[0037]

[0038] In the formula The value of the loss function; The number of training samples; This is the predicted output for the j-th sample; This represents the true output of the j-th sample. It is the Euclidean norm (L2 norm); The regularization parameters are set; This represents the weight value of the k-th protocol feature component in the protocol feature vector; This is the actual calculated value of the kth protocol feature component in the protocol feature vector of the energy storage device to be connected; This is the expected value of the k-th protocol feature component in the protocol rule base.

[0039] Step S4 specifically includes the following steps:

[0040] Integrity verification:

[0041] Based on a packet hash comparison mechanism, a hash value is calculated for each data packet during transmission, and integrity verification is performed. The hash value is calculated using the following formula. :

[0042]

[0043] In the formula The number of bytes in the data packet; This is the data value of the l-th byte; The byte-level key generated from the security policy parameters is the l-th byte; This is a bitwise XOR operation; This is a modulo operation used to constrain the numerical range of the calculation results;

[0044] Timing verification:

[0045] Based on statistical parameters of communication time, the timing consistency index is calculated using the following formula. :

[0046]

[0047] In the formula This represents the actual reception interval of the q-th frame; Let q be the theoretical reception interval for the q-th frame; The total number of communication frames; The maximum allowed interval;

[0048] Time sequence consistency index When the time sequence consistency index exceeds the set threshold, the time sequence check is considered passed; when the time sequence consistency index... If the value is not greater than the set threshold, the timing verification is deemed to have failed.

[0049] Security compliance verification:

[0050] The encryption algorithm used in the communication process is checked for compatibility: if it matches, the security compliance check is deemed to have passed; if it does not match, the security compliance check is deemed to have failed.

[0051] If the integrity check, timing check, and security compliance check all pass, the verification is considered successful, and the interface configuration parameters of the energy storage device to be connected are obtained; otherwise, return to step S3, regenerate the initial interface configuration parameters, and perform verification again.

[0052] Step S5 specifically includes the following steps:

[0053] The protocol rule base is updated based on an incremental update scheme.

[0054] During the update, the degree of difference is calculated using the following formula. :

[0055]

[0056] In the formula The protocol feature vector corresponding to the interface configuration parameters obtained in step S4; For the protocol rule base and The closest reference feature vector; It is the Euclidean norm (i.e., the L2 norm);

[0057] when When the value exceeds the set threshold, a determination is made. This is a completely new protocol feature vector, and new corresponding entries are created in the protocol rule base; when If the value is not greater than the set threshold, only the corresponding version information and security mechanism features will be updated.

[0058] When a device protocol version update is detected, the pre-trained deep neural network model is retrained; during retraining, the weight factors are calculated using the following formula:

[0059]

[0060] In the formula Let be the weight factor of the b-th sample at time t+1; The set attenuation coefficient; Let be the time interval between the b-th sample and the current time.

[0061] The adaptive configuration method for the communication interface of the energy storage system further includes the following steps:

[0062] S6. Based on packet loss rate, timing consistency, and security verification failure rate, determine the health of the communication process and update the parameters accordingly.

[0063] Step S6 specifically includes the following steps:

[0064] The communication health index is calculated using the following formula. :

[0065]

[0066] In the formula This is the first weighting coefficient set; The packet loss rate within the set time window; This is the second weighting coefficient that is set; It is a time-series consistency index; This is the set third weighting coefficient; To reduce the failure rate of security verification;

[0067] According to the communication health index To determine the health status of the communication process:

[0068] If the communication health index If the value is not less than the set threshold, the communication process is considered healthy.

[0069] If the communication health index If the value is less than the set threshold, the communication process is deemed unhealthy, and the protocol rule base in step S5 is updated. During the update process, the generated interface configuration parameters are updated using the following formula:

[0070]

[0071] In the formula Configure parameters for the updated interface; Configure parameters for the generated interface; The learning section is set up for later; for Based on current abnormal data The error gradient.

[0072] This invention also provides a system for implementing an adaptive configuration method for the communication interface of the energy storage system, comprising a data acquisition module, a vector generation module, a parameter generation module, a parameter verification module, an interface configuration module, and a parameter update module; the data acquisition module, vector generation module, parameter generation module, parameter verification module, interface configuration module, and parameter update module are connected in series; the data acquisition module is used to acquire communication protocol data information of the energy storage device to be connected and upload the data information to the vector generation module; the vector generation module is used to calculate the protocol feature vector of the energy storage device to be connected based on the received data information, according to the acquired data information, based on the encoding entropy scheme, communication time threshold, and encryption scheme data, and upload the data information to the parameter generation module; the parameter generation module is used to calculate the protocol feature vector of the energy storage device to be connected based on the received data information and the obtained protocol feature vector, Based on a pre-trained deep neural network model, initial interface configuration parameters for the energy storage devices to be connected are generated, and the data information is uploaded to the parameter verification module. The parameter verification module verifies the integrity, timing, and security compliance of the generated initial interface configuration parameters based on the received data information, obtains the interface configuration parameters for the energy storage devices to be connected, and uploads the data information to the interface configuration module. The interface configuration module updates the protocol rule base based on the received data information and the obtained interface configuration parameters, using an incremental update scheme, and completes the adaptive configuration of the communication interface of the energy storage devices to be connected, and uploads the data information to the parameter update module. The parameter update module determines the health of the communication process based on the received data information, considering packet loss rate, timing consistency, and security verification failure rate, and updates the parameters accordingly.

[0073] The adaptive configuration method and system for the communication interface of the energy storage system provided by this invention acquires and analyzes communication protocol data from multiple dimensions, and designs corresponding interface parameter generation, verification and update schemes. This not only realizes the adaptive configuration of the communication interface of the energy storage system, but also has higher reliability, better accuracy and higher efficiency. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0075] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0076] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The adaptive configuration method for the communication interface of the energy storage system disclosed in this invention includes the following steps:

[0077] S1. Obtain the communication protocol data information of the energy storage device to be connected; specifically including the following steps:

[0078] Obtain communication protocol data information of the energy storage device to be connected;

[0079] The communication protocol data information includes data format characteristics, timing logic characteristics, and security mechanism characteristics;

[0080] The data format features include data bit length, check bit type, byte order, and encoding rules; the timing logic features include communication frame interval time; and the security mechanism features include encryption algorithm type, authentication protocol version, and access control policy.

[0081] S2. Based on the data obtained in step S1, and using the encoding entropy scheme, communication time threshold, and encryption scheme data, calculate the protocol feature vector of the energy storage device to be connected; including the following steps:

[0082] The coding entropy formula is used to calculate the coding entropy of the energy storage device to be connected.

[0083] Based on statistical data of communication frame interval time, the stability coefficient of the energy storage device to be connected is calculated.

[0084] Based on the encryption algorithm type, authentication protocol version, and access control policy, the comprehensive security strength coefficient of the energy storage device to be connected is calculated.

[0085] The obtained encoding entropy, stability coefficient, and comprehensive security mechanism strength coefficient are used to construct the protocol feature vector of the energy storage device to be connected.

[0086] In practice, the following steps can be taken:

[0087] The coding entropy is calculated using the following formula. :

[0088]

[0089] In the formula Let be the probability of the i-th type of coded symbol appearing during the communication process; n is the number of types of coded symbols. Used to reflect the balance and information density of the protocol coding distribution;

[0090] The stability coefficient is calculated using the following formula. :

[0091]

[0092] In the formula The standard deviation of the communication frame interval time; This represents the average time interval between communication frames; The closer the value is to 1, the higher the timing stability;

[0093] The comprehensive strength coefficient of the safety mechanism is calculated using the following formula. :

[0094]

[0095] In the formula Weighting coefficients for encryption algorithm types; This is a quantization value for the strength of the encryption algorithm. In practice, it can be assigned a value from 0 to 1 according to the security level of the encryption algorithm. The weighting factor for the authentication protocol version; The security quantification value for the set authentication protocol version can be assigned from 0 to 1 based on the security and compatibility of the protocol during specific implementation. The weighting coefficients for access control policies; This is a quantification value for the strictness of the access control policy. In practice, it can be assigned a value from 0 to 1 according to the granularity of the policy.

[0096] Encode entropy Stability coefficient Comprehensive strength coefficient of security mechanism The protocol feature vector of the energy storage device to be connected is constructed. for ;

[0097] S3. Based on the protocol feature vector obtained in step S2, and using a pre-trained deep neural network model, generate the initial interface configuration parameters for the energy storage device to be connected; specifically including the following steps:

[0098] A pre-trained deep neural network based on attention weights is used to process the protocol feature vector obtained in step S2 to generate the initial interface configuration parameters of the energy storage device to be connected.

[0099] When training a deep neural network based on attention weights, the following formula is used as the loss function for the training process:

[0100]

[0101] In the formula The value of the loss function; The number of training samples; This is the predicted output for the j-th sample; This represents the true output of the j-th sample. It is the Euclidean norm (L2 norm); The regularization parameters are set; This represents the weight value of the k-th protocol feature component in the protocol feature vector; This is the actual calculated value of the kth protocol feature component in the protocol feature vector of the energy storage device to be connected; Let be the expected value of the k-th protocol feature component in the protocol rule base; in this loss function, the first term minimizes the mean square error between the output and the real configuration to ensure the accuracy of the generated parameters, and the second term introduces the difference constraint between the feature vector and the historical rules to improve the matching stability of the new protocol.

[0102] S4. Verify the integrity, timing, and security compliance of the initial interface configuration parameters generated in step S3 to obtain the interface configuration parameters of the energy storage device to be connected; specifically including the following steps:

[0103] Integrity verification:

[0104] Based on a packet hash comparison mechanism, a hash value is calculated for each data packet during transmission, and integrity verification is performed. The hash value is calculated using the following formula. :

[0105]

[0106] In the formula The number of bytes in the data packet; This is the data value of the l-th byte; The byte-level key generated from the security policy parameters is the l-th byte; This is a bitwise XOR operation; For modulo operation, it is used to constrain the numerical range of the calculation result; by introducing a security key at the data packet level to participate in integrity calculation, this formula can simultaneously detect anomalies caused by transmission errors or unauthorized tampering, ensuring the authenticity and immutability of data on the transmission path;

[0107] Timing verification:

[0108] Based on statistical parameters of communication time, the timing consistency index is calculated using the following formula. :

[0109]

[0110] In the formula This represents the actual reception interval of the q-th frame; Let q be the theoretical reception interval for the q-th frame; The total number of communication frames; The maximum allowed interval; When the value is close to 1, it indicates that the timing is highly consistent; when it is below the threshold, it is considered that the timing is mismatched and a dynamic adjustment process is triggered.

[0111] Time sequence consistency index When the time sequence consistency index exceeds the set threshold, the time sequence check is considered passed; when the time sequence consistency index... If the value is not greater than the set threshold, the timing verification is deemed to have failed.

[0112] Security compliance verification:

[0113] The encryption algorithm used in the communication process is checked for compatibility: if it matches, the security compliance check is deemed to have passed; if it does not match, the security compliance check is deemed to have failed.

[0114] If the integrity check, timing check, and security compliance check all pass, the verification is considered successful, and the interface configuration parameters of the energy storage device to be connected are obtained; otherwise, return to step S3, regenerate the initial interface configuration parameters, and perform verification again.

[0115] S5. Based on the interface configuration parameters obtained in step S4, update the protocol rule base using an incremental update scheme, and complete the adaptive configuration of the communication interface of the energy storage device to be connected; specifically including the following steps:

[0116] The protocol rule base is updated based on an incremental update scheme.

[0117] During the update, the degree of difference is calculated using the following formula. :

[0118]

[0119] In the formula The protocol feature vector corresponding to the interface configuration parameters obtained in step S4; For the protocol rule base and The closest reference feature vector; It is the Euclidean norm (i.e., the L2 norm);

[0120] when When the value exceeds the set threshold, a determination is made. This is a completely new protocol feature vector, and new corresponding entries are created in the protocol rule base; when If the value is not greater than the set threshold, only the corresponding version information and security mechanism features are updated; this strategy ensures that the rule base retains only new protocol features with significant differences, avoiding redundant accumulation.

[0121] When a device protocol version update is detected, the pre-trained deep neural network model is retrained; during retraining, the weight factors are calculated using the following formula:

[0122]

[0123] In the formula Let be the weight factor of the b-th sample at time t+1; The set attenuation coefficient; The time interval between the b-th sample and the current time is denoted by ; decreasing the weight over time enables the model to adapt to the features of the new version protocol more quickly, thereby ensuring that the generated interface configuration parameters maintain high matching degree and high stability.

[0124] S6. Based on packet loss rate, timing consistency, and security verification failure rate, determine the health of the communication process and update the parameters accordingly; specifically, this includes the following steps:

[0125] The communication health index is calculated using the following formula. :

[0126]

[0127] In the formula This is the first weighting coefficient set. The packet loss rate within the set time window; This is the second weighting coefficient that is set; It is a time-series consistency index; This is the set third weighting coefficient; To reduce the failure rate of security verification;

[0128] According to the communication health index To determine the health status of the communication process:

[0129] If the communication health index If the value is not less than the set threshold, the communication process is considered healthy.

[0130] If the communication health index If the value is less than the set threshold, the communication process is deemed unhealthy, and the protocol rule base in step S5 is updated. During the update process, the generated interface configuration parameters are updated using the following formula:

[0131]

[0132] In the formula Configure parameters for the updated interface; Configure parameters for the generated interface; The learning section is set up for later; for Based on the current abnormal data The error gradient; this formula can minimize the error index related to communication anomalies within a finite number of iteration steps, enabling new parameters to pass verification more quickly in the multi-dimensional verification process and enter the stable communication stage, thereby achieving closed-loop linkage with the rule base update module.

[0133] The method of this invention introduces the quantitative calculation of coding entropy and temporal stability coefficient in the protocol feature extraction stage, thereby achieving a precise digital description of data format features, temporal logic features and security mechanism features. This makes the construction process of protocol feature vectors more discriminative and computable, thereby improving the accuracy of protocol matching and the adaptation efficiency of new device access.

[0134] The method of this invention achieves high-precision mapping from feature vectors to interface configuration parameters through a deep learning model, ensuring that the generalization ability and stability of the generated communication parameters, protocol conversion rules and security policy parameters are significantly enhanced in a multi-protocol environment.

[0135] The method of this invention achieves multi-dimensional protection of the validity and security of configuration parameters through triple verification of data packet integrity, timing consistency and security compliance, and introduces hash integrity calculation and timing consistency index quantification method involving keys, which significantly reduces the risk of communication failure due to parameter mismatch.

[0136] The method of this invention adopts a novel incremental rule base update strategy and combines it with an online learning mechanism for time decay weights, so that the protocol rule base can maintain simplification and high timeliness in the long term, avoid the interference of historical redundancy accumulation on the matching model, and improve the rapid adaptation capability of new version protocols.

[0137] The method of the present invention will be further described below with reference to an embodiment:

[0138] This embodiment uses a 100MW / 200MWh large-scale electrochemical energy storage power station as an application scenario. This power station needs to connect to equipment from three different manufacturers (Manufacturer A: Energy Storage Converter PCS; Manufacturer B: Battery Management System (BMS); and Manufacturer C: Environmental Monitoring Device). The communication protocols of these devices differ significantly (PCS uses Modbus-TCP, BMS uses IEC61850-7-410, and the environmental monitoring device uses a proprietary protocol). The experimental objective is to verify the device access efficiency, adaptation accuracy, and communication stability of the proposed solution in a multi-protocol environment.

[0139] Hardware environment: Deployment protocol feature extraction module (using industrial-grade edge gateway, CPU is ARM Cortex-A53, memory is 2GB), dynamic configuration generation module (server configuration is Intel Xeon E3-1230v5, memory is 16GB), communication verification module (build a simulated communication link, bandwidth is 100Mbps, latency is ≤10ms).

[0140] Software environment: The operating system is Linux Ubuntu 20.04 LTS, the protocol matching model is developed based on the TensorFlow 2.8 framework, and the rule base is stored in MySQL 8.0;

[0141] Test metrics: Device access time (from start data collection to parameter loading completion), protocol adaptation success rate (the proportion of generated parameters that pass verification), communication stability metrics (packet loss rate per unit time, timing consistency index Ct, communication health index Hc).

[0142] Raw communication protocol data was collected from three types of devices. Data was collected continuously for 24 hours from each type of device. The data volume is as follows:

[0143] PCS (Modbus-TCP): Collects 12,000 data packets, including data format characteristics (16-bit data length, CRC16 checksum, big-endian byte order), timing logic characteristics (500ms frame interval, 2s timeout threshold), and security mechanism characteristics (no encryption, basic authentication).

[0144] BMS (IEC61850-7-410): Collects 15,000 data packets, with data format characteristics (32-bit data length, no parity bit, little-endian byte order), timing logic characteristics (200ms frame interval, 1s timeout threshold), and security mechanism characteristics (AES-256 encryption algorithm, TLS1.3 authentication protocol).

[0145] Environmental monitoring device (proprietary protocol): Collects 8000 data packets, with data format characteristics (8-bit data length, parity check, mixed byte order), timing logic characteristics (1000ms frame interval, 3s timeout threshold), and security mechanism characteristics (SM4 encryption algorithm, proprietary authentication protocol).

[0146] Using a hierarchical parsing and multi-dimensional encoding approach, the three-dimensional protocol feature vectors of the three types of devices were calculated, and the results are shown in Table 1.

[0147]

[0148] Input the feature vector V into the deep neural network model based on attention weights to generate the following interface configuration parameters:

[0149] PCS: With a baud rate of 9600bps and an IP address of 192.168.1.10; Modbus-TCP register address mapping rules; Basic authentication password encryption strategy;

[0150] BMS: The sampling rate is 50Hz, and the IP address is 192.168.1.20. Mapping rules for the IEC61850 dataset; For AES-256 keys and TLS handshake process;

[0151] Environmental monitoring devices: With a baud rate of 4800bps and an IP address of 192.168.1.30; Rules for parsing private protocol fields; For SM4 keys and private authentication process;

[0152] The verification was performed in a simulated communication environment, and the results are shown in Table 2.

[0153]

[0154] The mapping relationship between the "feature vector-configuration parameter" of the three types of devices will be synchronized to the rule base as incremental data:

[0155] If the protocol differences of PCS, BMS, and environmental monitoring devices are all greater than the threshold of 0.2, create 3 new mapping entries.

[0156] No protocol version iteration was detected, so there is no need to trigger model retraining.

[0157] Real-time monitoring of communication status, simulating an abnormal scenario of "network fluctuations in environmental monitoring devices" (packet loss rate suddenly increases to 15%):

[0158] When an anomaly occurs, the communication health index is 0.6 (below the set threshold of 0.8), triggering an adjustment.

[0159] Extract abnormal data (features of lost frames, timing deviations), combine them with incremental data from the rule base, and generate new parameters Pnew (adjust the number of frame retransmissions to 3 and the timeout threshold to 4s).

[0160] Re-verification: The communication health index has rebounded to 0.95, the packet loss rate has dropped to 0.5%, communication has returned to stability, and the adjustment time is ≤500ms.

[0161] The final results analysis is shown in Table 3:

[0162]

[0163] As can be seen, the proposed solution achieves rapid access, high-precision adaptation, and rapid recovery from anomalies for multi-protocol devices in the application of this energy storage station. All indicators are significantly better than traditional solutions, fully demonstrating the feasibility and effectiveness of the solution, and can meet the communication interface configuration requirements of large-scale, multi-protocol environments in energy storage stations.

[0164] like Figure 2The diagram shows the functional modules of the system of the present invention: The system disclosed in this invention, which implements the adaptive configuration method for the communication interface of the energy storage system, includes a data acquisition module, a vector generation module, a parameter generation module, a parameter verification module, an interface configuration module, and a parameter update module; these modules are connected in series. The data acquisition module acquires the communication protocol data information of the energy storage device to be connected and uploads the data information to the vector generation module. The vector generation module calculates the protocol feature vector of the energy storage device to be connected based on the received data information, using the encoding entropy scheme, communication time threshold, and encryption scheme data, and uploads the data information to the parameter generation module. The parameter generation module calculates the protocol feature vector of the energy storage device to be connected based on the received data information and the obtained data, using the encoding entropy scheme, communication time threshold, and encryption scheme data, and uploads the data information to the parameter generation module. The parameter generation module calculates the protocol feature vector of the energy storage device to be connected based on the received data information and the obtained data. Based on the protocol feature vector and a pre-trained deep neural network model, the system generates initial interface configuration parameters for the energy storage devices to be connected and uploads the data to the parameter verification module. The parameter verification module verifies the integrity, timing, and security compliance of the generated initial interface configuration parameters based on the received data, obtaining the interface configuration parameters for the energy storage devices to be connected, and uploads the data to the interface configuration module. The interface configuration module updates the protocol rule base based on the received data and the obtained interface configuration parameters, using an incremental update scheme, and completes the adaptive configuration of the communication interface for the energy storage devices to be connected, uploading the data to the parameter update module. The parameter update module determines the health of the communication process based on the received data, considering packet loss rate, timing consistency, and security verification failure rate, and updates the parameters accordingly.

Claims

1. An adaptive configuration method for the communication interface of an energy storage system, characterized in that... Includes the following steps: S1. Obtain the communication protocol data information of the energy storage device to be connected; specifically including the following steps: Obtain communication protocol data information of the energy storage device to be connected; The communication protocol data information includes data format characteristics, timing logic characteristics, and security mechanism characteristics; The data format features include data bit length, check bit type, byte order, and encoding rules; the timing logic features include communication frame interval time; and the security mechanism features include encryption algorithm type, authentication protocol version, and access control policy. S2. Based on the data obtained in step S1, and using the encoding entropy scheme, communication time threshold, and encryption scheme data, calculate the protocol feature vector of the energy storage device to be connected; including the following steps: The coding entropy formula is used to calculate the coding entropy of the energy storage device to be connected. Based on statistical data of communication frame interval time, the stability coefficient of the energy storage device to be connected is calculated. Based on the encryption algorithm type, authentication protocol version, and access control policy, the comprehensive security strength coefficient of the energy storage device to be connected is calculated. The obtained encoding entropy, stability coefficient, and comprehensive security mechanism strength coefficient are used to construct the protocol feature vector of the energy storage device to be connected. The specific implementation includes the following steps: The coding entropy is calculated using the following formula. : In the formula Let be the probability of the i-th type of coded symbol appearing during the communication process; n is the number of types of coded symbols. The stability coefficient is calculated using the following formula. : In the formula The standard deviation of the communication frame interval time; This represents the average time interval between communication frames; The comprehensive strength coefficient of the safety mechanism is calculated using the following formula. : In the formula Weighting coefficients for encryption algorithm types; The quantization value for the set encryption algorithm strength; The weighting factor for the authentication protocol version; The security quantification value for the configured authentication protocol version; The weighting coefficients for access control policies; A quantifiable value representing the strictness of the set access control policy; Encode entropy Stability coefficient Comprehensive strength coefficient of security mechanism The protocol feature vector of the energy storage device to be connected is constructed. for ; S3. Based on the protocol feature vector obtained in step S2, generate the initial interface configuration parameters of the energy storage device to be connected based on the pre-trained deep neural network model; S4. Verify the integrity, timing, and security compliance of the initial interface configuration parameters generated in step S3 to obtain the interface configuration parameters of the energy storage device to be connected; specifically including the following steps: Integrity verification: Based on a packet hash comparison mechanism, a hash value is calculated for each data packet during transmission, and integrity verification is performed. The hash value is calculated using the following formula. : In the formula The number of bytes in the data packet; This is the data value of the l-th byte; The byte-level key generated from the security policy parameters is the l-th byte; This is a bitwise XOR operation; For modulo operation; Timing verification: Based on statistical parameters of communication time, the timing consistency index is calculated using the following formula. : In the formula This represents the actual reception interval of the q-th frame; Let q be the theoretical reception interval for the q-th frame; The total number of communication frames; The maximum allowed interval; Time sequence consistency index When the time sequence consistency index exceeds the set threshold, the time sequence check is considered passed; when the time sequence consistency index... If the value is not greater than the set threshold, the timing verification is deemed to have failed. Security compliance verification: The encryption algorithm used in the communication process is checked for compatibility: if it matches, the security compliance check is deemed to have passed; if it does not match, the security compliance check is deemed to have failed. If the integrity check, timing check, and security compliance check all pass, the verification is considered successful, and the interface configuration parameters of the energy storage device to be connected are obtained; otherwise, return to step S3, regenerate the initial interface configuration parameters, and perform verification again. S5. Based on the interface configuration parameters obtained in step S4, update the protocol rule base using an incremental update scheme, and complete the adaptive configuration of the communication interface of the energy storage device to be connected; specifically including the following steps: The protocol rule base is updated based on an incremental update scheme. During the update, the degree of difference is calculated using the following formula. : In the formula The protocol feature vector corresponding to the interface configuration parameters obtained in step S4; For the protocol rule base and The closest reference feature vector; It is the Euclidean norm; when When the value exceeds the set threshold, a determination is made. This is a completely new protocol feature vector, and new corresponding entries are created in the protocol rule base; when If the value is not greater than the set threshold, only the corresponding version information and security mechanism features will be updated. When a device protocol version update is detected, the pre-trained deep neural network model is retrained; during retraining, the weight factors are calculated using the following formula: In the formula Let be the weight factor of the b-th sample at time t+1; The set attenuation coefficient; Let be the time interval between the b-th sample and the current time.

2. The adaptive configuration method for the communication interface of an energy storage system according to claim 1, characterized in that... Step S3 specifically includes the following steps: A pre-trained deep neural network based on attention weights is used to process the protocol feature vector obtained in step S2 to generate the initial interface configuration parameters of the energy storage device to be connected. When training a deep neural network based on attention weights, the following formula is used as the loss function for the training process: In the formula The value of the loss function; The number of training samples; This is the predicted output for the j-th sample; This represents the true output of the j-th sample. It is the Euclidean norm; The regularization parameters are set; This represents the weight value of the k-th protocol feature component in the protocol feature vector; This is the actual calculated value of the kth protocol feature component in the protocol feature vector of the energy storage device to be connected; This is the expected value of the k-th protocol feature component in the protocol rule base.

3. The adaptive configuration method for the communication interface of an energy storage system according to claim 2, characterized in that... It also includes the following steps: S6. Based on packet loss rate, timing consistency, and security verification failure rate, determine the health of the communication process and update the parameters accordingly.

4. The adaptive configuration method for the communication interface of an energy storage system according to claim 3, characterized in that... Step S6 specifically includes the following steps: The communication health index is calculated using the following formula. : In the formula This is the first weighting coefficient set. The packet loss rate within the set time window; This is the second weighting coefficient that is set; It is a time-series consistency index; This is the set third weighting coefficient; To reduce the failure rate of security verification; According to the communication health index To determine the health status of the communication process: If the communication health index If the value is not less than the set threshold, the communication process is considered healthy. If the communication health index If the value is less than the set threshold, the communication process is deemed unhealthy, and the protocol rule base in step S5 is updated. During the update process, the generated interface configuration parameters are updated using the following formula: In the formula Configure parameters for the updated interface; Configure parameters for the generated interface; The learning section is set up for later; for Based on the current abnormal data The error gradient.

5. A system for implementing the adaptive configuration method of the communication interface of an energy storage system as described in any one of claims 1 to 4, characterized in that... It includes a data acquisition module, a vector generation module, a parameter generation module, a parameter verification module, an interface configuration module, and a parameter update module; the data acquisition module, vector generation module, parameter generation module, parameter verification module, interface configuration module, and parameter update module are connected in series; the data acquisition module is used to acquire the communication protocol data information of the energy storage device to be connected and upload the data information to the vector generation module; The vector generation module is used to calculate the protocol feature vector of the energy storage device to be connected based on the received data information, the encoding entropy scheme, the communication time threshold, and the encryption scheme data, and then upload the data information to the parameter generation module. The parameter generation module is used to generate the initial interface configuration parameters of the energy storage device to be connected based on the received data information, the obtained protocol feature vector, and a pre-trained deep neural network model, and upload the data information to the parameter verification module. The parameter verification module is used to verify the integrity, timing and security compliance of the generated initial interface configuration parameters based on the received data information, obtain the interface configuration parameters of the energy storage device to be connected, and upload the data information to the interface configuration module. The interface configuration module is used to update the protocol rule base based on the received data information and the obtained interface configuration parameters, according to the incremental update scheme, and to complete the adaptive configuration of the communication interface of the energy storage device to be connected, and upload the data information to the parameter update module. The parameter update module is used to determine the health of the communication process based on the received data information, including packet loss rate, timing consistency, and security verification failure rate, and then update the parameters accordingly.

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