Standardized access and detection system for auxiliary control equipment of transformer substation

Abstract

The invention discloses a standardized access and detection system for auxiliary control equipment of a transformer substation, and relates to the field of transformer substations. Comprising a multi-protocol adaptation layer which constructs a protocol state machine and supports dynamic analysis and conversion of DL / T860 and MQTT protocols; performing model difference quantification, designing a double-similarity algorithm, and outputting a model compatibility scoring report; containerized simulation clusters, deployment of equipment simulators based on Docker and support of thousand-level equipment concurrent testing are carried out, and in model difference quantification, a verification process comprises the steps of analyzing equipment ICD files and extracting a model field list; and loading the MICS document of the corresponding equipment from the standard model library. According to the method, the protocol state machine is constructed, then compatibility scoring is performed based on double-similarity calculation, so that the compatibility verification efficiency can be improved, in addition, an analysis quintuple is set in the protocol adaptation layer, the analysis speed is increased, finally resource allocation and data generation simulation are performed through the simulator, and access whole-process automatic testing is realized.

Description

Technical Field

[0001] This invention relates to the field of substations, and more particularly to a standardized access and detection system for auxiliary control equipment in substations. Background Technology

[0002] With the rapid development of smart grids, the types and quantities of auxiliary control equipment in substations (such as environmental monitoring devices, security systems, fire-fighting equipment, and intelligent inspection robots) have increased dramatically. These auxiliary control devices undertake core functions such as substation environmental parameter acquisition, equipment status monitoring, and safety early warning. Their reliable access and real-time monitoring are crucial to ensuring the safe and stable operation of substations.

[0003] Existing testing systems rely on manual configuration of test cases, making them incompatible with devices from multiple vendors and resulting in low efficiency in protocol compatibility verification. The lack of automated tools for converting private models during the upgrade of existing stations leads to long integration cycles and high error rates.

[0004] Therefore, this invention proposes a standardized access and detection system for auxiliary control equipment in substations. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a standardized access and detection system for substation auxiliary control equipment.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A standardized access and testing system for auxiliary control equipment in substations includes:

[0008] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0009] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0010] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0011] Preferably, in the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0012] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the termination state, corresponding to: Q = {q0, q1, q2, q3};

[0013] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0014] δ is the state transition function;

[0015] F is the set of termination states.

[0016] Preferably, in the model difference quantification, the verification process is as follows:

[0017] A1: Parse the device ICD file and extract the model field list;

[0018] A2: Load the corresponding device's MICS document from the standard model library;

[0019] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0020] Preferably, in step A3, the difference comparison algorithm is as follows: in:

[0021] f i For model attributes (such as name, data type, and unit);

[0022] δ(f i std ,f i dev ) is the attribute matching function, f i std For standard model properties, f i dev For device model attributes;

[0023] w i This refers to attribute weights.

[0024] Preferably, in the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a perfect match, and 0 otherwise.

[0025] Preferably, the containerized simulation cluster includes:

[0026] Simulator deployment involves each Docker container running a device simulation instance and allocating resources accordingly.

[0027] Data injection simulates sensor data generation, and the generated data is then injected for simulation. Preferably, in the simulator deployment, the resource allocation formula is: in:

[0028] N container The number of Docker containers to be created;

[0029] N device The total number of physical devices to be simulated;

[0030] R cpu Simulate the CPU resource requirements of a single device instance;

[0031] C node This refers to the total CPU capacity of a single-node server or virtual machine.

[0032] Preferably: In the data injection, the formula for generating simulated sensor data is: D sim =D base +λ·σ·randn(), where:

[0033] D base This is a baseline value (e.g., temperature 25°C).

[0034] λ is the noise figure;

[0035] σ is the standard deviation of the equipment range (e.g., if the temperature sensor range is ±10℃, then σ=10);

[0036] randn() generates a random number from a standard normal distribution.

[0037] Preferably, the λ in the formula for generating analog sensor data in the data injection is (0.1, 0.2).

[0038] The beneficial effects of this invention are as follows:

[0039] 1. This invention improves the efficiency of compatibility verification by constructing a protocol state machine and then performing compatibility scoring based on dual similarity calculation. In addition, setting a parsing quintuple in the protocol adaptation layer increases the parsing speed. Finally, resource allocation and data generation simulation are performed through a simulator to achieve automated testing of the entire access process. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of a standardized access and detection system architecture for substation auxiliary control equipment proposed in this invention. Detailed Implementation

[0041] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0042] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "setting" should be interpreted broadly. For example, they can refer to a fixed connection or setting, a detachable connection or setting, or an integral connection or setting. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0043] Example 1:

[0044] A standardized access and testing system for auxiliary control equipment in substations includes:

[0045] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0046] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0047] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0048] Example 2:

[0049] A standardized access and testing system for auxiliary control equipment in substations includes:

[0050] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0051] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0052] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0053] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0054] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the final state, corresponding to: Q = {q0, q1, q2, q3};

[0055] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0056] δ is the state transition function;

[0057] F is the set of termination states.

[0058] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0059]

[0060] Example 3:

[0061] A standardized access and testing system for auxiliary control equipment in substations includes:

[0062] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0063] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0064] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0065] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0066] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the termination state, corresponding to: Q = {q0, q1, q2, q3};

[0067] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0068] δ is the state transition function;

[0069] F is the set of termination states.

[0070] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0071]

[0072] In the model difference quantification, the verification process is as follows:

[0073] A1: Parse the device ICD file and extract the model field list;

[0074] A2: Load the corresponding device's MICS document from the standard model library;

[0075] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0076] Example 4:

[0077] A standardized access and testing system for auxiliary control equipment in substations includes:

[0078] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0079] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0080] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0081] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0082] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the termination state, corresponding to: Q = {q0, q1, q2, q3};

[0083] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0084] δ is the state transition function;

[0085] F is the set of termination states.

[0086] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0087]

[0088] In the model difference quantification, the verification process is as follows:

[0089] A1: Parse the device ICD file and extract the model field list;

[0090] A2: Load the corresponding device's MICS document from the standard model library;

[0091] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0092] In step A3, the difference comparison algorithm is as follows: in:

[0093] f i For model attributes (such as name, data type, and unit);

[0094] δ(f i std ,f i dev ) is the attribute matching function, f i std For standard model properties, f i dev For device model attributes;

[0095] w i This refers to attribute weights.

[0096] In the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a complete match, and 0 otherwise.

[0097] Example 5:

[0098] A standardized access and testing system for auxiliary control equipment in substations includes:

[0099] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0100] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0101] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0102] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0103] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the final state, corresponding to: Q = {q0, q1, q2, q3};

[0104] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0105] δ is the state transition function;

[0106] F is the set of termination states.

[0107] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0108]

[0109] In the model difference quantification, the verification process is as follows:

[0110] A1: Parse the device ICD file and extract the model field list;

[0111] A2: Load the corresponding device's MICS document from the standard model library;

[0112] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0113] In step A3, the difference comparison algorithm is as follows: in:

[0114] f i For model attributes (such as name, data type, and unit);

[0115] δ(f i std ,f i dev ) is the attribute matching function, f i std For standard model properties, f i devFor device model attributes;

[0116] w i This refers to attribute weights.

[0117] In the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a complete match, and 0 otherwise.

[0118] The containerized simulation cluster includes:

[0119] Simulator deployment involves each Docker container running a device simulation instance and allocating resources accordingly.

[0120] Data injection simulates sensor data generation, and the generated data is then injected for simulation.

[0121] Example 6:

[0122] A standardized access and testing system for auxiliary control equipment in substations includes:

[0123] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0124] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0125] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0126] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0127] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the termination state, corresponding to: Q = {q0, q1, q2, q3};

[0128] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0129] δ is the state transition function;

[0130] F is the set of termination states.

[0131] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0132]

[0133] In the model difference quantification, the verification process is as follows:

[0134] A1: Parse the device ICD file and extract the model field list;

[0135] A2: Load the corresponding device's MICS document from the standard model library;

[0136] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0137] In step A3, the difference comparison algorithm is as follows: in:

[0138] f i For model attributes (such as name, data type, and unit);

[0139] δ(f i std ,f i dev ) is the attribute matching function, f i std For standard model properties, f i dev For device model attributes;

[0140] w i This refers to attribute weights.

[0141] In the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a complete match, and 0 otherwise.

[0142] The containerized simulation cluster includes:

[0143] Simulator deployment involves each Docker container running a device simulation instance and allocating resources accordingly.

[0144] Data injection simulates sensor data generation, and the generated data is then injected for simulation.

[0145] In the simulator deployment, the resource allocation formula is as follows: in:

[0146] N container The number of Docker containers to be created;

[0147] N device The total number of physical devices to be simulated;

[0148] R cpu Simulate the CPU resource requirements of a single device instance;

[0149] C node This refers to the total CPU capacity of a single-node server or virtual machine.

[0150] Example 7:

[0151] A standardized access and testing system for auxiliary control equipment in substations includes:

[0152] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0153] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0154] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0155] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0156] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the final state, corresponding to: Q = {q0, q1, q2, q3};

[0157] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0158] δ is the state transition function;

[0159] F is the set of termination states.

[0160] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0161]

[0162] In the model difference quantification, the verification process is as follows:

[0163] A1: Parse the device ICD file and extract the model field list;

[0164] A2: Load the corresponding device's MICS document from the standard model library;

[0165] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0166] In step A3, the difference comparison algorithm is as follows: in:

[0167] f i For model attributes (such as name, data type, and unit);

[0168] δ(f i std ,f i dev ) is the attribute matching function, f istd For standard model properties, f i dev For device model attributes;

[0169] w i This refers to attribute weights.

[0170] In the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a complete match, and 0 otherwise.

[0171] The containerized simulation cluster includes:

[0172] Simulator deployment involves each Docker container running a device simulation instance and allocating resources accordingly.

[0173] Data injection simulates sensor data generation, and the generated data is then injected for simulation.

[0174] In the simulator deployment, the resource allocation formula is as follows: in:

[0175] N container The number of Docker containers to be created;

[0176] N device The total number of physical devices to be simulated;

[0177] R cpu Simulate the CPU resource requirements of a single device instance;

[0178] C node This refers to the total CPU capacity of a single-node server or virtual machine.

[0179] In the data injection, the formula for generating simulated sensor data is: D sim =D base +λ·σ·randn(), where:

[0180] D base This is a baseline value (e.g., temperature 25°C).

[0181] λ is the noise figure;

[0182] σ is the standard deviation of the equipment range (e.g., if the temperature sensor range is ±10℃, then σ=10);

[0183] randn() generates a random number from a standard normal distribution.

[0184] Example 8:

[0185] A standardized access and testing system for auxiliary control equipment in substations includes:

[0186] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0187] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0188] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0189] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0190] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the termination state, corresponding to: Q = {q0, q1, q2, q3};

[0191] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0192] δ is the state transition function;

[0193] F is the set of termination states.

[0194] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0195]

[0196] In the model difference quantification, the verification process is as follows:

[0197] A1: Parse the device ICD file and extract the model field list;

[0198] A2: Load the corresponding device's MICS document from the standard model library;

[0199] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0200] In step A3, the difference comparison algorithm is as follows: in:

[0201] f i For model attributes (such as name, data type, and unit);

[0202] δ(f i std ,f i dev ) is the attribute matching function, f i std For standard model properties, f i devFor device model attributes;

[0203] w i This refers to attribute weights.

[0204] In the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a complete match, and 0 otherwise.

[0205] The containerized simulation cluster includes:

[0206] Simulator deployment involves each Docker container running a device simulation instance and allocating resources accordingly.

[0207] Data injection simulates sensor data generation, and the generated data is then injected for simulation.

[0208] In the simulator deployment, the resource allocation formula is as follows: in:

[0209] N container The number of Docker containers to be created;

[0210] N device The total number of physical devices to be simulated;

[0211] R cpu Simulate the CPU resource requirements of a single device instance;

[0212] C node This refers to the total CPU capacity of a single-node server or virtual machine.

[0213] In the data injection, the formula for generating simulated sensor data is: D sim =D base +λ·σ·randn(), where:

[0214] D base This is a baseline value (e.g., temperature 25°C).

[0215] λ is the noise figure;

[0216] σ is the standard deviation of the equipment range (e.g., if the temperature sensor range is ±10℃, then σ=10);

[0217] randn() generates a random number from a standard normal distribution.

[0218] The formula for generating analog sensor data in the data injection is λ = 0.1.

[0219] Example 9:

[0220] A standardized access and testing system for auxiliary control equipment in substations includes:

[0221] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0222] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0223] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0224] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0225] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the final state, corresponding to: Q = {q0, q1, q2, q3};

[0226] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0227] δ is the state transition function;

[0228] F is the set of termination states.

[0229] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0230]

[0231] In the model difference quantification, the verification process is as follows:

[0232] A1: Parse the device ICD file and extract the model field list;

[0233] A2: Load the corresponding device's MICS document from the standard model library;

[0234] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0235] In step A3, the difference comparison algorithm is as follows: in:

[0236] f i For model attributes (such as name, data type, and unit);

[0237] δ(f i std ,f i dev ) is the attribute matching function, f i std For standard model properties, f i devFor device model attributes;

[0238] w i This refers to attribute weights.

[0239] In the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a complete match, and 0 otherwise.

[0240] The containerized simulation cluster includes:

[0241] Simulator deployment involves each Docker container running a device simulation instance and allocating resources accordingly.

[0242] Data injection simulates sensor data generation, and the generated data is then injected for simulation.

[0243] In the simulator deployment, the resource allocation formula is as follows: in:

[0244] N container The number of Docker containers to be created;

[0245] N device The total number of physical devices to be simulated;

[0246] R cpu Simulate the CPU resource requirements of a single device instance;

[0247] C node This refers to the total CPU capacity of a single-node server or virtual machine.

[0248] In the data injection, the formula for generating simulated sensor data is: D sim =D base +λ·σ·randn(), where:

[0249] D base This is a baseline value (e.g., temperature 25°C).

[0250] λ is the noise figure;

[0251] σ is the standard deviation of the equipment range (e.g., if the temperature sensor range is ±10℃, then σ=10);

[0252] randn() generates a random number from a standard normal distribution.

[0253] The formula for generating analog sensor data in the data injection has λ = 0.15.

[0254] Example 10:

[0255] A standardized access and testing system for auxiliary control equipment in substations includes:

[0256] A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols;

[0257] Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output.

[0258] A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

[0259] In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where:

[0260] Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the final state, corresponding to: Q = {q0, q1, q2, q3};

[0261] ∑ is the input symbol set, ∑={Header,Data,Checksum,End};

[0262] δ is the state transition function;

[0263] F is the set of termination states.

[0264] For example, for the DL / T860 message 68 04 00 00 00 00 68 01 02…, the state machine executes sequentially:

[0265]

[0266] In the model difference quantification, the verification process is as follows:

[0267] A1: Parse the device ICD file and extract the model field list;

[0268] A2: Load the corresponding device's MICS document from the standard model library;

[0269] A3: Execute the difference comparison algorithm to generate a compatibility report.

[0270] In step A3, the difference comparison algorithm is as follows: in:

[0271] f i For model attributes (such as name, data type, and unit);

[0272] δ(f i std ,f i dev ) is the attribute matching function, f i std For standard model properties, f i devFor device model attributes;

[0273] w i This refers to attribute weights.

[0274] In the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a complete match, and 0 otherwise.

[0275] The containerized simulation cluster includes:

[0276] Simulator deployment involves each Docker container running a device simulation instance and allocating resources accordingly.

[0277] Data injection simulates sensor data generation, and the generated data is then injected for simulation.

[0278] In the simulator deployment, the resource allocation formula is as follows:

[0279] in:

[0280] N container The number of Docker containers to be created;

[0281] N device The total number of physical devices to be simulated;

[0282] R cpu Simulate the CPU resource requirements of a single device instance;

[0283] C node This refers to the total CPU capacity of a single-node server or virtual machine.

[0284] In the data injection, the formula for generating simulated sensor data is: D sim =D base +λ·σ·randn(), where:

[0285] D base This is a baseline value (e.g., temperature 25°C).

[0286] λ is the noise figure;

[0287] σ is the standard deviation of the equipment range (e.g., if the temperature sensor range is ±10℃, then σ=10);

[0288] randn() generates a random number from a standard normal distribution.

[0289] The formula for generating analog sensor data in the data injection has λ = 0.2.

[0290] Existing testing systems rely on manual configuration of test cases, making them incompatible with devices from multiple vendors. Furthermore, protocol compatibility verification is inefficient, and the conversion of proprietary models during the upgrade of existing stations lacks automated tools, resulting in long integration cycles and high error rates.

[0291] This invention improves compatibility verification efficiency by constructing a protocol state machine and then performing compatibility scoring based on dual similarity calculation. In addition, setting a parsing quintuple in the protocol adaptation layer increases parsing speed. Finally, resource allocation and data generation simulation are performed through a simulator to achieve fully automated testing of the access process.

[0292] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A standardized access and testing system for auxiliary control equipment in substations, characterized in that, include: A multi-protocol adaptation layer is built to construct a protocol state machine, supporting dynamic parsing and conversion of DL / T860 and MQTT protocols; Model difference quantification is performed, a dual similarity algorithm is designed, and a model compatibility score report is output. A containerized simulated cluster, based on Docker, deploys device simulators and supports concurrent testing of thousands of devices.

2. The standardized access and testing system for substation auxiliary control equipment according to claim 1, characterized in that, In the multi-protocol adaptation layer, the protocol parsing process is modeled as a quintuple: P = (Q, ∑, δ, q0, F), where: Q is a set of states, which includes the initial state, the parsing header, the parsing data, and the termination state, corresponding to: Q = {q0, q1, q2, q3}; ∑ is the input symbol set, ∑={Header,Data,Checksum,End}; δ is the state transition function; F is the set of termination states.

3. The standardized access and testing system for substation auxiliary control equipment according to claim 1, characterized in that, In the model difference quantification, the verification process is as follows: A1: Parse the device ICD file and extract the model field list; A2: Load the corresponding device's MICS document from the standard model library; A3: Execute the difference comparison algorithm to generate a compatibility report.

4. The standardized access and testing system for substation auxiliary control equipment according to claim 3, characterized in that, In step A3, the difference comparison algorithm is as follows: in: f i For model attributes (such as name, data type, and unit); δ(f i std ,f i dev ) is the attribute matching function, f i std For standard model properties, f i dev For device model attributes; w i This refers to attribute weights.

5. A standardized access and testing system for substation auxiliary control equipment according to claim 4, characterized in that, In the difference comparison algorithm of step A3, the attribute matching function is 1 when there is a complete match, and 0 otherwise.

6. The standardized access and testing system for substation auxiliary control equipment according to claim 1, characterized in that, The containerized simulation cluster includes: Simulator deployment involves each Docker container running a device simulation instance and allocating resources accordingly. Data injection simulates sensor data generation, and the generated data is then injected for simulation.

7. A standardized access and testing system for substation auxiliary control equipment according to claim 6, characterized in that, In the simulator deployment, the resource allocation formula is as follows: in: N container The number of Docker containers to be created; N device The total number of physical devices to be simulated; R cpu Simulate the CPU resource requirements of a single device instance; C node This refers to the total CPU capacity of a single-node server or virtual machine.

8. A standardized access and testing system for substation auxiliary control equipment according to claim 6, characterized in that, In the data injection, the formula for generating simulated sensor data is: D sim =D base +λ·σ·randn(), where: D base This is a baseline value (e.g., temperature 25°C). λ is the noise figure; σ is the standard deviation of the equipment range (e.g., if the temperature sensor range is ±10℃, then σ=10); randn() generates a random number from a standard normal distribution.

9. A standardized access and testing system for substation auxiliary control equipment according to claim 8, characterized in that, The λ in the formula for generating analog sensor data in the data injection is (0.1, 0.2).

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