Performance test system and method based on power security gateway

Abstract

The invention discloses a performance test system and method based on an electric power security gateway, relates to the technical field of gateway performance test, and solves the technical problems that the test process of an existing test scheme is complicated, and the test result is unreliable due to the fact that different working environments of the electric power security gateway cannot be tested. The method comprises the following steps: training an artificial intelligence model by utilizing environment test data and simulation performance data to obtain a performance prediction model; the performance prediction model can predict the performance parameters of the security gateways of the same model or the same batch, and the reliability of the model is relatively high due to the fact that enough reliable data is constructed for training; the influence of each power grid device is considered when the environmental area range is determined, extreme environment data is obtained based on the area environment range combination, and gateway performance data of a target gateway in the extreme environment data is calculated through a performance prediction model; according to the scheme, the target gateway can be tested in all directions without being limited by environmental data and network fluctuation at a certain moment.

Description

Technical Field

[0001] The present application belongs to the field of gateway performance testing technology, and relates to technology, specifically a performance testing system and method based on a power safety gateway. Background Art

[0002] Security gateways are widely used in the power industry. By deploying security gateways at the network boundary to ensure the security, confidentiality and data integrity of cross-network information transmission, effective authentication, authorization and data transmission between clients and servers are achieved. It can be seen that the reliability of security gateways is very important for the power industry.

[0003] The performance of a security gateway generally needs to be tested, and the test indicators include throughput, latency, packet loss rate, etc. The existing test scheme mainly sends a pre-built test data stream, calculates the test indicators by detecting the relevant data during the transmission of the data test stream, and then judges the performance of the security gateway based on the test indicators. During the test of the security gateway, each test indicator will be affected by factors such as the environment and network. Even if the test indicators meet the requirements, it can only mean that the performance of the security gateway can meet the requirements in the current application environment. If performance testing is to be performed in multiple application scenarios and multiple application environments, a large number of test data streams need to be constructed to complete multiple tests, which makes the performance testing process of the security gateway very complicated and the testing cost is relatively high compared to a single security gateway.

[0004] The present application provides a performance testing system and method based on a power safety gateway to solve the above-mentioned technical problems. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a performance testing system and method based on a power safety gateway, which is used to solve the technical problems that the existing testing scheme has a complex testing process and cannot test different working environments of the power safety gateway, resulting in unreliable test results.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a performance testing system based on a power safety gateway, including a performance testing module, and a data acquisition module connected thereto;

[0007] Data collection module: used to extract target gateway information and its target application scenario; wherein the target gateway information includes the security gateway model and standard usage environment; and,

[0008] Used to build environmental test data based on the standard usage environment, simulate the working status of the target gateway based on the environmental test data, and obtain model training data;

[0009] Performance testing module: used to build an artificial intelligence model, train the artificial intelligence model through model training data, and obtain a performance prediction model; and,

[0010] Used to build extreme environment data of the target gateway based on the target application scenario; identify gateway performance data under extreme environment data through the performance prediction model; and evaluate the performance of the target gateway based on the gateway performance data.

[0011] Preferably, the environmental test data is constructed based on the standard usage environment, including:

[0012] Based on the extraction of the range of each environmental factor in the standard use environment, the range of the environmental factor is divided in turn according to the set interval to obtain a number of environmental factor data points; wherein the environmental factors include ambient temperature, relative humidity and network parameters;

[0013] A plurality of environmental factor data points corresponding to each environmental factor are combined and processed to obtain a plurality of environmental test data; wherein the environmental test data includes all environmental factor types.

[0014] Preferably, before dividing the scope of environmental factors in sequence, the scope of environmental factors is expanded, including:

[0015] Simulate and obtain the influence of each power device in the power network structure on the working environment of the security gateway, and extract the maximum influence value; wherein the maximum influence value corresponds to at least one of the environmental elements;

[0016] The corresponding environmental factor range is expanded by at least one times of the maximum impact value to obtain an expanded environmental factor range.

[0017] Preferably, simulating the working state of the target gateway based on the environmental test data includes:

[0018] Building a simulation environment of the target gateway through a simulation platform; adjusting the simulation environment according to the environmental test data, and obtaining simulation performance data of the target gateway through a preset data flow simulation; wherein the simulation platform includes JMeter or LoadRunner;

[0019] Integrate the environmental test data and the corresponding simulation performance data into simulation training data; integrate all simulation training data obtained from the simulation into model training data.

[0020] Preferably, the extreme environment data of the target gateway is constructed based on the target application scenario, including:

[0021] Determine the target usage environment of the target gateway through the target application scenario; wherein the target application scenario refers to the usage area and the network topology structure of the target gateway;

[0022] The extreme values ​​corresponding to each environmental factor are extracted from the target use environment, and the extreme values ​​of each environmental factor are combined to generate extreme environment data; wherein the extreme values ​​include maximum values ​​and minimum values.

[0023] Preferably, the target use environment of the target gateway is determined by the target application scenario, including:

[0024] Determine the regional environmental scope according to the use area in the target application scenario; wherein the regional environmental scope includes the corresponding scope of each environmental element;

[0025] Simulate the impact on the target gateway in the network topology, quantify the impact into corresponding values ​​of environmental factors and mark them as quantitative results; expand the regional environmental range through the quantitative results to obtain the target usage environment.

[0026] Preferably, the extreme values ​​of each environmental factor are combined, including:

[0027] The extreme value of each environmental factor is regarded as a value box; wherein the value box includes the maximum value and the minimum value of the corresponding environmental factor;

[0028] A value is extracted from each value box, and the values ​​extracted from each value box are combined into an extreme data group; several extreme data groups are integrated into extreme environment data.

[0029] Preferably, evaluating the performance of the target gateway based on the gateway performance data includes:

[0030] After preprocessing, the extreme environment data is input into the performance prediction model to obtain gateway performance data; wherein the number of gateway performance data is consistent with the number of extreme data groups in the extreme environment data;

[0031] Compare the gateway performance data with the performance parameter threshold to evaluate whether the performance of the target gateway meets the requirements; wherein the performance parameter threshold is set based on the working requirements of the target gateway.

[0032] Preferably, when the performance of the target gateway does not meet the requirements, the corresponding extreme environment data is marked as a key monitoring environment; early warning measures are executed for the key monitoring environment; wherein the early warning measures include environmental adjustment or network adjustment.

[0033] The second aspect of the present application provides a performance testing method based on a power safety gateway, including:

[0034] Extract target gateway information and its target application scenario; the target gateway information includes the security gateway model and standard usage environment; build environmental test data based on the standard usage environment, simulate the working state of the target gateway based on the environmental test data, and obtain model training data;

[0035] Build an artificial intelligence model, train the artificial intelligence model with model training data, and obtain a performance prediction model; build extreme environment data of the target gateway based on the target application scenario;

[0036] Identify gateway performance data under extreme environment data through performance prediction models; evaluate the performance of the target gateway based on the gateway performance data.

[0037] Compared with the prior art, the beneficial effects of this application are:

[0038] The present application can expandably construct environmental test data according to the standard use environment of the target gateway, and can simulate the simulated performance data of the target gateway under various environmental test data by using a simulation platform; use paired environmental test data and simulated performance data to complete the training of the artificial intelligence model to obtain a performance prediction model; the performance prediction model can predict the performance parameters of security gateways of the same model or batch, and because sufficiently reliable data is constructed for training, the reliability of the model is relatively high; after constructing the performance prediction model, the present application analyzes and mines the target application scenario of the target gateway to determine its corresponding regional environmental range, and considers the influence of various power grid equipment when determining the environmental regional range, so as to determine that the regional environmental range is more reasonable; then, extreme environmental data is obtained based on the regional environmental range combination, and the gateway performance data of the target gateway in the extreme environmental data is calculated by the performance prediction model, and then it is determined whether the performance meets the requirements; the present solution can conduct a comprehensive test on the target gateway without being restricted by environmental data and network fluctuations at a certain moment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 This is a schematic diagram of the method steps of the performance testing system in Example 1 of the present application;

[0041] Figure 2 This is a schematic diagram of the system principle of the performance testing system in Example 1 of the present application;

[0042] Figure 3 This is a schematic diagram of the steps of the method for obtaining model training data in Example 1 of the present application;

[0043] Figure 4 Schematic diagram of the method steps for obtaining extreme environment data in Example 1 of the present application. DETAILED DESCRIPTION

[0044] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0045] In the performance test of the security gateway, the current solution is mainly to test the performance indicators of the gateway between the client and the server through the test flow interaction between the two. The problems with this test solution are as follows: the security gateway is already connected to the network for testing, and once the performance parameters do not meet the standards, it needs to be replaced or even re-selected; and the test process is affected by a variety of environments, such as the environment where the gateway is located, network conditions, etc. The environment may fluctuate at any time. Even if the current security gateway performance meets the standards, there is no guarantee that its performance parameters will continue to meet the standards as the environment changes.

[0046] In response to the above technical problems, this application re-examines the performance testing plan of the security gateway, adjusts the performance testing plan, and proposes a performance testing system and method for the power security gateway.

[0047] Embodiment 1:

[0048] See also Figure 1-Figure 2 , the first aspect of the present application provides a performance testing system based on a power safety gateway, including a performance testing module, and a data acquisition module connected thereto;

[0049] Data acquisition module: used to extract target gateway information and its target application scenarios; and used to build environmental test data based on the standard usage environment, simulate the working status of the target gateway based on the environmental test data, and obtain model training data;

[0050] Performance testing module: used to build an artificial intelligence model, train the artificial intelligence model through model training data, and obtain a performance prediction model; and, used to build extreme environment data of the target gateway based on the target application scenario; identify gateway performance data under extreme environment data through the performance prediction model; and evaluate the performance of the target gateway based on the gateway performance data.

[0051] In this embodiment, the data acquisition module is mainly responsible for data collection and preprocessing, and the collected data includes target gateway information and its target application scenario. The target gateway information includes the security gateway model and standard use environment, etc. The target gateway is the gateway that needs to be performance tested. The standard use environment can be extracted from the instructions for use in the target gateway. The standard use environment mainly includes temperature, humidity, network conditions (network parameters) and other environmental factors that affect the performance of the target gateway. The target application scenario refers to the actual scenario in which the target gateway or the corresponding batch of target gateways is about to be used. For example, if it is set in a certain power network, the target application scenario can be a related scenario provided by the user to simulate whether the performance of the target gateway in the related scenario can meet the requirements, or it can be the actual use scenario of the target gateway to detect whether the target gateway will cause its performance to be unstable in the actual use scenario due to the fluctuation of environmental factors.

[0052] The performance test module is mainly responsible for building a performance prediction model based on the artificial intelligence model, and using this performance prediction model to analyze whether the target gateway meets the performance requirements in various extreme environments. The performance test module can be built based on a cloud server, and only the data acquisition module needs to provide the necessary data to complete the performance test.

[0053] Compared with the existing test scheme, this embodiment does not need to actually send data streams between the client and the server for performance testing, but simulates the environment in which the target gateway is located, simulates the sending of data test streams to test the performance parameters of the target gateway, which can effectively reduce the impact of the test process on the working status of the target gateway, and can also test the changes in the performance parameters of the target gateway as environmental factors change.

[0054] See also Figure 3 In order to construct sufficient simulation data, this embodiment processes the standard usage environment of the target gateway. The specific processing flow is as follows:

[0055] Based on the extraction of the range of each environmental factor in the standard use environment, the environmental factor range is divided in turn according to the set interval to obtain a number of environmental factor data points; the several environmental factor data points corresponding to each environmental factor are combined and processed to obtain a number of environmental test data.

[0056] The standard use environment is the working environment range of the target gateway given by the manufacturer. Theoretically, the performance parameters of the target gateway working in the standard use environment can meet the relevant regulations, but the regulations may not completely match the requirements in the actual use process. This mismatch is mainly reflected in the fact that the regulations generally stipulate the performance lower limit of the security gateway, such as the qualified requirements of packet loss rate, throughput, latency, stability, etc., but the requirements for security gateways in the power network are higher. If the performance lower limit is used as a reference, it is difficult to ensure that its performance can consistently meet the requirements of the power network.

[0057] The above environmental factors mainly include ambient temperature, relative humidity and network parameters. Each environmental factor corresponds to a data range. In order to predict the performance parameters of the target gateway in various environments, the range of each environmental factor is divided according to the set interval. Each environmental factor corresponds to multiple environmental factor points. Then, one environmental factor point corresponding to each environmental factor is extracted, and after combining, an environmental test data can be obtained. After multiple combinations, several environmental test data can be obtained.

[0058] When using a security gateway, the user also refers to its standard use environment, compares the standard use environment with the installation environment where the security gateway will be used, and determines that the installation environment is within the standard use environment. However, when the power network is more complex, the working conditions of various power equipment will also affect the working environment of the security gateway, and because the power network covers a wide range, the security gateway may be set up in various extreme environments. After considering this error, the range of each environmental factor of the target gateway can be expanded.

[0059] Before dividing the scope of environmental factors in turn, the scope of environmental factors is expanded, including: simulating the impact of each power device in the power network structure on the working environment of the security gateway and extracting the maximum impact value; expanding the corresponding scope of environmental factors by at least one times the maximum impact value to obtain the expanded scope of environmental factors.

[0060] The impact of each power device on the working environment of the security gateway can be obtained through simulation or through statistics of historically collected data. The maximum impact value corresponds to at least one environmental factor, that is, the power equipment in the power network will have an impact on at least one environmental factor during operation, such as temperature or humidity, and the maximum impact value is the additional impact of each power device in the power network on the environmental factor in addition to the climate environment. After simulating and obtaining the maximum impact value, the endpoints of the corresponding environmental factor range are expanded by at least one times the maximum impact value (it may be one endpoint or two endpoints) to expand the range of environmental factors. The expanded range of environmental factors can take into account the impact of the power network on the working environment of the target gateway, and the performance parameters simulated by this environmental factor range are more in line with the actual application scenario.

[0061] It should be noted that the above maximum impact value can be obtained in the following ways:

[0062] 1. Analysis of the topology of the power network. By analyzing the topology of the power network, the connection relationship and mutual influence between each device and the security gateway can be determined. Graph theory and network analysis methods can be used to calculate indicators such as path length and node importance between devices to evaluate the impact of each device on the security gateway. For example, betweenness centrality can be used to measure the importance of a device in the power network, that is, the frequency of the device appearing on the shortest path between other devices. Devices with high betweenness centrality have a greater impact on the stability of the network and the working environment of the security gateway.

[0063] 2. Physical model and simulation method. Establish a physical model of the power network to simulate the operating status and interaction of each device. Specifically, you can use power system simulation software, such as PSSE (Power System Simulation for Engineering), to perform dynamic simulation of the power network and analyze the impact of each device on the working environment of the security gateway.

[0064] There are many models or methods in the existing solutions that can obtain the maximum impact value, which will not be described here.

[0065] After determining the environmental test data, it is necessary to simulate the target gateway's simulated performance data under each environmental test data, and then integrate the environmental test data and the corresponding simulated performance data into simulated training data. The simulated performance data can be obtained through the following process:

[0066] Build a simulation environment for the target gateway through the simulation platform; adjust the simulation environment according to the environmental test data, and obtain the simulation performance data of the target gateway through the preset data flow simulation; integrate the environmental test data and the corresponding simulation performance data into simulation training data; integrate all simulation training data obtained from the simulation into model training data.

[0067] Exemplarily, the LoadRunner simulation platform is used to simulate the simulated performance data corresponding to the target gateway under each environment test data;

[0068] 1. Build a simulation environment: Use LoadRunner's virtual user generator to create a Web script to simulate access to the target gateway. You can set the number of virtual users, such as 100. Add logic to the Web script to simulate different temperatures and humidity levels, and send these values ​​as request parameters to the target gateway. Use the platform's network settings function to simulate different network delays and bandwidth limitations.

[0069] 2. Adjust the simulation environment: Adjust the simulation environment according to the environmental test data. You can use a single variable adjustment method, such as adjusting the temperature first, while keeping other environmental factors unchanged. View the simulation performance data of the target gateway through the platform controller. The simulation performance data includes throughput, latency, etc.

[0070] 3. Integrate and obtain simulation training data: extract simulation performance data and relevant parameters of the corresponding simulation environment. Each simulation performance data and the corresponding relevant parameters can be integrated into one simulation training data, and all the obtained simulation training data are integrated into model training data.

[0071] The simulation of the working performance of the target gateway in different environments is not limited to the LoadRunner simulation platform, but can also be simulated through other platforms such as JMeter. Each platform has its limitations. For example, the LoadRunner simulation platform cannot directly adjust the temperature and humidity. These parameters can be modified through scripts to complete the simulation of different environments. Moreover, the above environmental test data includes all types of environmental factors, that is, every environmental factor that affects the working status of the target gateway should be included.

[0072] After obtaining the model training data through the simulation of the aforementioned scheme, the constructed artificial intelligence model is trained through the model training data to obtain a performance prediction model. The artificial intelligence model mainly includes a BP neural network model, an RBF neural network model, etc. The training process of the artificial intelligence model is disclosed in many existing schemes. This embodiment has no special requirements for the training of the artificial intelligence model, so the training process is no longer described in detail.

[0073] The input data of the performance prediction model obtained through training is environmental test data, including parameters that affect the working state of the target gateway, such as temperature, humidity, and network parameters, and the output data is simulated performance data. The training process is set up so that the artificial intelligence model can identify the mapping relationship between the performance of the target gateway and its environment. Both environmental test data and simulated performance data need to be preprocessed during the training process so that the model can recognize them.

[0074] In summary, the environmental test data can be expanded and constructed according to the standard use environment of the target gateway, and the simulated performance data of the target gateway under each environmental test data can be simulated by using the simulation platform; the artificial intelligence model is trained using paired environmental test data and simulated performance data to obtain a performance prediction model. The performance prediction model can predict the performance parameters of security gateways of the same model or batch, and because sufficiently reliable data is constructed for training, the model has high reliability.

[0075] Next, we will explain in detail how to use the performance prediction model to predict the gateway performance of the target gateway.

[0076] The traditional testing scheme uses the actually deployed security gateway as the test object, tests the various performance indicators of the security gateway by sending test data streams, and judges whether the performance of the security gateway meets the requirements based on the test results. This process has the following problems: the security gateway needs to actually process the test data, which will occupy certain resources and affect the gateway's processing of other data. In addition, the test process also includes data processing, and the test results have a certain delay; in addition, each test result corresponds to a specific test environment. Once the test environment, such as temperature, humidity, network conditions, etc., changes, it is necessary to retest. Therefore, the existing scheme cannot fully determine whether the security gateway can always meet the performance requirements in its working environment.

[0077] The technical idea of ​​this example is to first obtain the extreme environment corresponding to the target gateway. If the gateway performance data in the extreme environment can meet the requirements, its performance is reliable regardless of how the environmental factors change.

[0078] See also Figure 4 First, obtain the extreme environment corresponding to the target gateway, that is, build the extreme environment data of the target gateway based on the target application scenario, including:

[0079] The target use environment of the target gateway is determined through the target application scenario; the extreme values ​​corresponding to each environmental factor are extracted from the target use environment, and the extreme values ​​of each environmental factor are combined to generate extreme environment data.

[0080] The target application scenario refers to the target gateway's usage area and the network topology used. Based on the usage area, the environmental data corresponding to the target gateway's working location can be obtained, and the maximum and minimum values ​​corresponding to each environmental factor, such as the maximum temperature and the minimum temperature, can be extracted based on the environmental data. The network topology is used to identify the impact of other power grid equipment on the target gateway's working environment.

[0081] Determine the target usage environment of the target gateway through the target application scenario, which includes the following two parts:

[0082] Determine the regional environment scope based on the usage area in the target application scenario;

[0083] Simulate the impact on the target gateway in the network topology, quantify the impact into corresponding values ​​of environmental factors and mark them as quantitative results; expand the regional environmental range through the quantitative results to obtain the target usage environment.

[0084] The regional environmental range includes the corresponding range of each environmental element. For example, the historical temperature data, humidity data, network parameters, etc. of the corresponding area or location can be extracted according to the usage area. The maximum and minimum values ​​of the corresponding environmental elements, such as maximum temperature, minimum temperature, etc., can be extracted from the historical temperature data, historical humidity data, and network parameters.

[0085] However, historical temperature data and historical humidity data can be obtained through sensor detection or meteorological platforms, so the influence of other power grid equipment is not considered during the acquisition process. If the power grid equipment generates a lot of heat during operation and affects the target gateway, the heat will be coupled with the ambient temperature, making the actual operating temperature of the target gateway greater than the ambient temperature. Therefore, it is necessary to calculate the influence of other equipment in combination with the power grid topology.

[0086] Grid equipment may generate heat during operation, and this heat may affect the working environment of the target gateway during radiation. The degree of this influence can be obtained through simulation analysis. For example, the maximum influence of the grid equipment on the surrounding environment of the target gateway can be analyzed based on the grid topology. For example, the maximum temperature rise around the target gateway can be 0.5°C, and the regional environment range can be expanded using this 0.5°C.

[0087] Of course, quantitative results can also be obtained through data analysis. The following uses temperature as an example:

[0088] 1. Determine the influencing devices: According to the grid topology, determine which grid devices will affect the temperature of the target gateway's environment during operation. Assume that transformers and power cables are influencing devices.

[0089] 2. Establish a heat impact model, including heat generation model and heat propagation model;

[0090] Heat generation model: Calculate the heat generated by the device based on the power and efficiency of the device. For example, for a device with a power of P and an efficiency of η, the heat generated is Q = P × (1-η).

[0091] Heat propagation model: According to the way heat propagates, a heat propagation model is established. For example, for conduction propagation, Fourier's law can be used. Where k is the thermal conductivity of the material, A is the heat transfer area, is the temperature gradient.

[0092] 3. Calculate the quantitative results: Based on the heat propagation model, calculate the temperature rise of the environment around the security gateway.

[0093] For example, assume that the power grid topology is as shown in the figure below, the security gateway is located at node A, and other devices are located at nodes B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, and Z.

[0094] Calculate the impact of device B:

[0095] Power of device B: 100kW;

[0096] Efficiency of device B: 90%;

[0097] Heat generated by device B: Q B =100×(1-0.9)=10kW;

[0098] The distance between device B and security gateway: 10m;

[0099] Heat transfer efficiency: 0.5;

[0100] Temperature rise of the environment around the security gateway: ΔT B =10×0.5 / 4π×0.04×10≈1.0℃;

[0101] Calculate the impact of device C:

[0102] Power of device C: 200kW

[0103] Efficiency of device C: 95%

[0104] Heat generated by device C: Q C =200×(1-0.95)=10kW;

[0105] The distance between device C and security gateway: 20m;

[0106] Heat transfer efficiency: 0.5;

[0107] Temperature rise of the environment around the security gateway: ΔT C =10×0.5 / 4π×0.04×20≈0.5℃;

[0108] Total temperature rise: ΔT total =ΔT B +ΔT C =1.0+0.5=1.5℃;

[0109] In this example, device B and device C increase the temperature of the security gateway's surrounding environment by 1.0°C and 0.5°C respectively, with a total temperature increase of 1.5°C.

[0110] The regional environmental range can be expanded by combining the quantitative results of the corresponding environmental factors. The expanded regional environmental range takes into account both the climate environment and the impact of the power grid equipment on the climate environment. In other words, the expanded regional environmental range is the possible working environment of the target gateway during actual operation. It should be noted that the impact of the power grid equipment on the working environment may be limited to individual environmental factors, or some impacts cannot be quantified. If they cannot be quantified, a larger quantitative result can be set.

[0111] Each environmental factor in the expanded regional environmental range corresponds to an extreme value, either one extreme value or two extreme values. For example, temperature can correspond to two extreme values: maximum temperature and minimum temperature.

[0112] Take out one extreme value of each environmental factor and combine them to get an extreme data group. Each extreme data group includes an extreme value of each environmental factor. The multiple extreme data groups obtained by combination are integrated into extreme environmental data. After preprocessing the extreme environmental data, the network performance data of the target gateway in the extreme environment can be obtained. If the network performance data still meets the requirements, it means that the performance of the target gateway in the target application scenario can meet the power grid data transmission requirements.

[0113] After constructing the performance prediction model, this embodiment analyzes and mines the target application scenario of the target gateway to determine its corresponding regional environmental range, and considers the influence of each power grid device when determining the environmental regional range, so as to determine that the regional environmental range is more reasonable. Then, based on the regional environmental range combination, the extreme environmental data is obtained, and the gateway performance data of the target gateway in the extreme environmental data is calculated through the performance prediction model to determine whether the performance meets the requirements. This embodiment can perform a full range of tests on the target gateway without being limited by environmental data and network fluctuations at a certain moment.

[0114] Embodiment 2: Based on Embodiment 1, this embodiment provides a processing method when the gateway performance data of the target gateway does not meet the requirements, specifically: when the performance of the target gateway does not meet the requirements, the corresponding extreme environment data is marked as a key monitoring environment; early warning measures are executed for the key monitoring environment; wherein the early warning measures include environmental adjustment or network adjustment.

[0115] When the gateway performance parameters of the target gateway under a certain extreme data group do not meet the requirements, and the probability of the occurrence of the extreme data group is low (the probability of the extreme values ​​of each environmental factor occurring at the same time can be determined by statistical historical data), there is no need to replace the target gateway, and the working environment of the target gateway is monitored in real time. If the real-time monitored environmental factors gradually approach the extreme values ​​in the above extreme data group, the working environment of the target gateway is temporarily intervened through the set early warning measures, so that its working environment will not reach the situation corresponding to the above extreme data group.

[0116] The second aspect of the present application provides a performance testing method based on a power safety gateway, including:

[0117] Extract target gateway information and its target application scenario; the target gateway information includes the security gateway model and standard usage environment; build environmental test data based on the standard usage environment, simulate the working state of the target gateway based on the environmental test data, and obtain model training data;

[0118] Build an artificial intelligence model, train the artificial intelligence model with model training data, and obtain a performance prediction model; build extreme environment data of the target gateway based on the target application scenario;

[0119] Identify gateway performance data under extreme environment data through performance prediction models; evaluate the performance of the target gateway based on the gateway performance data.

[0120] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. The performance test system based on the power safety gateway is characterized by: It includes a performance test module and a data acquisition module connected thereto; Data collection module: used to extract target gateway information and its target application scenario; wherein the target gateway information includes the security gateway model and standard usage environment; and, Used to construct environmental test data based on the standard usage environment, simulate the working state of the target gateway based on the environmental test data, and obtain model training data; Performance testing module: used to build an artificial intelligence model, train the artificial intelligence model through the model training data, and obtain a performance prediction model; and Used to construct extreme environment data of the target gateway based on the target application scenario; identify gateway performance data under the extreme environment data through the performance prediction model; and evaluate the performance of the target gateway based on the gateway performance data.

2. The performance testing system based on the power safety gateway according to claim 1 is characterized in that: Build environmental test data based on the standard usage environment, including: Based on the range of each environmental factor extracted from the standard use environment, the range of the environmental factor is divided in turn according to the set interval to obtain a number of environmental factor data points; wherein the environmental factors include ambient temperature, relative humidity and network parameters; A plurality of environmental element data points corresponding to each of the environmental elements are combined and processed to obtain a plurality of environmental test data; wherein the environmental test data includes all environmental element types.

3. The performance testing system based on the power safety gateway according to claim 2 is characterized in that: Before dividing the scope of the environmental factors in sequence, the scope of the environmental factors is expanded, including: Simulate and obtain the influence of each power device in the power network structure on the working environment of the security gateway, and extract the maximum influence value; wherein the maximum influence value corresponds to at least one of the environmental elements; The corresponding environmental factor range is expanded by at least one times of the maximum impact value to obtain an expanded environmental factor range.

4. The performance testing system based on the power safety gateway according to claim 3 is characterized in that: Simulating the working state of the target gateway based on the environmental test data includes: Building a simulation environment of the target gateway through a simulation platform; adjusting the simulation environment according to the environmental test data, and obtaining simulation performance data of the target gateway through a preset data flow simulation; wherein the simulation platform includes JMeter or LoadRunner; The environmental test data and the corresponding simulation performance data are integrated into simulation training data; and all simulation training data obtained by simulation are integrated into the model training data.

5. The performance testing system based on the power safety gateway according to claim 2 is characterized in that: Constructing extreme environment data of the target gateway based on the target application scenario includes: Determine the target usage environment of the target gateway through the target application scenario; wherein the target application scenario refers to the usage area and the network topology structure used by the target gateway; The extreme values ​​corresponding to the environmental elements are extracted from the target use environment, and the extreme values ​​of the environmental elements are combined to generate extreme environment data; wherein the extreme values ​​include maximum values ​​and minimum values.

6. The performance testing system based on the power safety gateway according to claim 5 is characterized in that: The target use environment of the target gateway is determined by the target application scenario, including: Determine the regional environment range according to the usage area in the target application scenario; wherein the regional environment range includes the corresponding range of each of the environmental elements; The impact on the target gateway in the network topology is simulated, and the impact is quantified into a numerical value corresponding to the environmental factor and marked as a quantified result; the regional environmental range is expanded according to the quantified result to obtain a target usage environment.

7. The performance testing system based on the electric power safety gateway according to claim 6 is characterized in that: The extreme values ​​of each of the environmental factors are combined, including: The extreme value of each environmental factor is used as a value box; wherein the value box includes the maximum value and the minimum value of the corresponding environmental factor; A value is extracted from each of the value boxes, and the values ​​extracted from the value boxes are combined into an extreme data group; and a plurality of extreme data groups are integrated into the extreme environment data.

8. The performance testing system based on the electric power safety gateway according to claim 7 is characterized in that: Evaluate the performance of the target gateway based on the gateway performance data, including: Inputting the extreme environment data into the performance prediction model after preprocessing to obtain gateway performance data; wherein the number of gateway performance data is consistent with the number of extreme data groups in the extreme environment data; The gateway performance data is compared with a performance parameter threshold to evaluate whether the performance of the target gateway meets the requirements; wherein the performance parameter threshold is set based on the working requirements of the target gateway.

9. The performance testing system based on the electric power safety gateway according to claim 8 is characterized in that: When the performance of the target gateway does not meet the requirements, the corresponding extreme environment data is marked as a key monitoring environment; and early warning measures are executed for the key monitoring environment; wherein the early warning measures include environmental adjustment or network adjustment.

10. A performance testing method based on a power safety gateway, based on the performance testing system based on a power safety gateway according to any one of claims 1 to 9, characterized in that: include: Extract target gateway information and its target application scenario; wherein the target gateway information includes the security gateway model and the standard usage environment; construct environmental test data based on the standard usage environment, simulate the working state of the target gateway based on the environmental test data, and obtain model training data; Constructing an artificial intelligence model, training the artificial intelligence model with the model training data to obtain a performance prediction model; constructing extreme environment data of the target gateway based on the target application scenario; The gateway performance data under the extreme environment data is identified by the performance prediction model; and the performance of the target gateway is evaluated based on the gateway performance data.

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