Wireless data acquisition method and device of multi-source tester, terminal equipment and computer readable storage medium

Abstract

The invention discloses a wireless data collection method and device of a multi-source tester, terminal equipment and a computer readable storage medium, and belongs to the field of data management.The method comprises the steps that the type of each tester is obtained, and initial collection parameters are determined; performing correlation analysis on the acquired data of each tester based on the initial acquisition parameters, determining a data dependency relationship among the testers, constructing a directed acyclic graph, and determining a change rate transfer coefficient of each edge; calculating a sampling data change rate of each tester; and for each pair of combination of the source node and the target node, when the change rate of the sampling data of the source node exceeds the change rate of the target node, adjusting the sampling parameter of the target node according to the change rate transfer coefficient, otherwise, taking the initial sampling parameter of the target node as the final acquisition parameter. By implementing the invention, the sampling parameters can be dynamically adjusted, and the problem of data change caused by the fact that the fixed acquisition parameters cannot capture the relevance of the test instrument in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of data management, and in particular to a wireless data acquisition method, device, terminal equipment and computer-readable storage medium of a multi-source tester. Background Art

[0002] In power systems, substation equipment testing is a critical component in ensuring safe and stable grid operation. In recent years, with the advancement of the Internet of Things and wireless communication technologies, data acquisition systems based on wireless sensor networks have been increasingly used in substation testing. These systems typically employ a distributed data acquisition architecture, collecting and transmitting various test data such as DC resistance, insulation resistance, and dielectric loss via wireless data acquisition terminals.

[0003] However, in actual substation equipment testing, data collection from multiple, heterogeneous test instruments faces complex correlation challenges. For example, the correlation between DC resistance testers and insulation resistance testers is crucial for power equipment status diagnosis. Their measured data is strongly coupled. This means that abnormal fluctuations in DC resistance (such as increased contact resistance) can directly or indirectly cause deviations in insulation resistance measurements, leading to misjudgments of dielectric loss. Using fixed acquisition parameters may not accurately capture data changes caused by these correlations, and the collected data may contain errors, impacting subsequent data analysis and equipment status diagnosis. Summary of the Invention

[0004] The embodiments of the present invention provide a wireless data acquisition method, apparatus, terminal device and computer-readable storage medium for a multi-source tester, which can dynamically adjust sampling parameters to improve the accuracy of multi-source data acquisition, thereby solving the problem in the prior art that when fixed acquisition parameters are used, multi-source heterogeneous test instruments cannot accurately capture data changes caused by correlation due to strong data coupling, resulting in errors in the collected data.

[0005] An embodiment of the present invention provides a wireless data acquisition method for a multi-source tester, comprising:

[0006] Get the type of each tester;

[0007] Determine the initial acquisition parameters of each tester according to the type of each tester, so that each tester performs data acquisition based on the initial acquisition parameters to obtain the acquired data of each tester; the acquisition parameters include: sampling frequency, data format and acquisition timing;

[0008] Perform correlation analysis on the collected data of each tester to determine the data dependency relationship between the testers, and construct a directed acyclic graph based on the data dependency relationship between the testers; wherein the nodes in the directed acyclic graph represent the testers, and the edges in the directed acyclic graph represent the data dependency relationship;

[0009] Inputting the directed acyclic graph into a preset transfer coefficient calculation model so that the transfer coefficient calculation model generates a change rate transfer coefficient of each edge according to the directed acyclic graph;

[0010] According to the collected data of each tester, the sampling data change rate of each tester is calculated;

[0011] For each pair of source nodes and target nodes, when the sampling data change rate of the source node exceeds the sampling data change rate of the target node, the sampling parameters of the target node are adjusted according to the change rate transfer coefficient of each edge between the source node and the target node, and the adjusted acquisition parameters are used as the final acquisition parameters. Otherwise, the initial sampling parameters of the target node are used as the final acquisition parameters; wherein, the source node is used to represent the node in the directed acyclic graph that only has outgoing edges but no incoming edges; the target node is used to represent the node other than the source node in the directed acyclic graph.

[0012] Furthermore, the type of each tester is obtained, including:

[0013] Obtain wireless communication signals of each tester;

[0014] Determine the communication characteristics of each tester based on the wireless communication signals of each tester; the communication characteristics include: communication protocol characteristics, data frame format and response timing characteristics;

[0015] The communication characteristics of each tester are converted to obtain the characteristic vector of each tester;

[0016] For each tester, the Euclidean distance between the feature vector of the tester and the feature vectors of each type of reference tester in the preset tester feature fingerprint library is calculated to obtain several feature similarity ratios;

[0017] The type of the tester is determined based on several feature similarity ratios.

[0018] Furthermore, after the adjusted acquisition parameters are used as the final acquisition parameters, the following steps are further included:

[0019] Instructing each tester to collect data based on the final collection parameters to obtain test data of each tester;

[0020] For each tester, the ratio of the amplitude difference of the test data in adjacent sampling periods to the sampling period is calculated to obtain the fluctuation coefficient of each sampling period;

[0021] When the difference in the fluctuation coefficients of adjacent sampling periods exceeds a preset fluctuation threshold, it is determined that the test data has a sudden change in value; otherwise, it is determined that the test data does not have a sudden change in value;

[0022] For each tester, calculate the ratio of the communication interval of the test data to the average communication period to obtain the communication time ratio;

[0023] When the communication time ratio exceeds the preset interval threshold, it is determined that the test data has a signal interruption, otherwise, it is determined that the test data does not have a signal interruption;

[0024] For each tester, the Euclidean distance of the eigenvectors of the test data in adjacent data segments is calculated to obtain the eigenvector distance;

[0025] When the feature vector distance is less than a preset distance threshold, it is determined that the test data has data duplication; otherwise, it is determined that the test data does not have data duplication.

[0026] Furthermore, before each tester is instructed to perform data acquisition based on the final acquisition parameters and obtains the test data of each tester, the method further includes:

[0027] For each tester, the collected data of the tester is analyzed in time series to obtain the timeliness index of the tester;

[0028] Perform feature extraction on the collected data of the tester to obtain several feature parameters;

[0029] Conduct principal component analysis on several characteristic parameters to obtain the data value index of the tester;

[0030] Calculate the ratio of the tester's timeliness index and the data value index to obtain the tester's priority coefficient;

[0031] Determine the resource allocation order of each tester according to the descending order of priority coefficients;

[0032] Select target testers with the same priority coefficient;

[0033] Obtain the communication bandwidth occupancy rate, processor load rate and memory space occupancy rate of the target tester during data acquisition based on the initial acquisition parameters;

[0034] Determine the system resource utilization rate based on the communication bandwidth utilization rate, processor load rate and memory space utilization rate;

[0035] Obtain sampling data from the target tester, determine the continuity score of data sampling, the losslessness score of data sequence, and the standardization score of data format;

[0036] Determine data integrity requirements based on the continuity score of data sampling, the losslessness score of data sequence, and the standardization score of data format;

[0037] Calculate the ratio of the target tester's data integrity requirement to the system resource occupancy rate to obtain the target tester's resource allocation coefficient;

[0038] Determine the resource allocation order of the target tester according to the descending order of the resource allocation coefficients;

[0039] Based on the resource allocation order of the target tester, the resource allocation order of the corresponding tester in the resource allocation order of each tester is updated to obtain the final resource allocation order of each tester;

[0040] According to the final resource allocation order of each tester, resources are allocated to the data acquisition process of each tester based on the final acquisition parameters.

[0041] Furthermore, the wireless data acquisition method of the multi-source tester further includes:

[0042] Detect whether the test data has value mutations, signal interruptions, or data duplications;

[0043] When the test data has a sudden change in value, the sampling frequency is adjusted according to the fluctuation coefficient;

[0044] When there is a signal interruption in the test data, switch the communication channel;

[0045] When there is data duplication in the test data, the data transmission interval is adjusted according to the feature vector distance.

[0046] Furthermore, the transfer coefficient calculation model is determined by the following method:

[0047] Acquire a number of training data; each training data includes: a directed acyclic training graph and actual change rate transfer coefficients of each edge thereof;

[0048] A number of training data are input into the transfer coefficient calculation model to be trained, so that the transfer coefficient calculation model to be trained takes the directed acyclic training graph as input and the predicted change rate transfer coefficient of each edge in the directed acyclic training graph as output for training, and during the training process, the network parameters of the transfer coefficient calculation model are adjusted according to the predicted change rate transfer coefficient of each edge and the corresponding actual change rate transfer coefficient until the loss function converges to obtain the preset transfer coefficient calculation model.

[0049] Furthermore, after obtaining the test data of each tester, the following steps are also included:

[0050] Establish a classification index for the test data and send the classification index to the substation detection management platform.

[0051] Based on the above method embodiment, the present invention provides a corresponding device embodiment, including: a tester type acquisition module, an initial acquisition parameter determination module, a dependency analysis module, a transfer coefficient calculation module, a data change rate calculation module, and a sampling parameter adjustment module;

[0052] A tester type acquisition module is used to obtain the type of each tester;

[0053] The initial acquisition parameter determination module is used to determine the initial acquisition parameters of each tester according to the type of each tester, so that each tester performs data acquisition based on the initial acquisition parameters to obtain the collected data of each tester; the acquisition parameters include: sampling frequency, data format and acquisition timing;

[0054] A dependency analysis module is used to perform correlation analysis on the collected data of each tester, determine the data dependency between the testers, and construct a directed acyclic graph based on the data dependency between the testers; wherein the nodes in the directed acyclic graph represent the testers, and the edges in the directed acyclic graph represent the data dependency;

[0055] A transfer coefficient calculation module is used to input the directed acyclic graph into a preset transfer coefficient calculation model so that the transfer coefficient calculation model generates a change rate transfer coefficient of each edge according to the directed acyclic graph;

[0056] The data change rate calculation module is used to calculate the sampling data change rate of each tester based on the collected data of each tester;

[0057] The sampling parameter adjustment module is used to adjust the sampling parameters of the target node according to the change rate transfer coefficient of each edge between the source node and the target node for each pair of source node and target node combination, when the sampling data change rate of the source node exceeds the sampling data change rate of the target node, and use the adjusted acquisition parameters as the final acquisition parameters; otherwise, use the initial sampling parameters of the target node as the final acquisition parameters; wherein, the source node is used to represent a node in the directed acyclic graph that only has outgoing edges but no incoming edges; the target node is used to represent a node other than the source node in the directed acyclic graph.

[0058] Based on the above-mentioned method embodiment, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of the wireless data acquisition method of the multi-source tester as described in the present invention are implemented.

[0059] Based on the above-mentioned method embodiment, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to execute the steps of the wireless data acquisition method of the multi-source tester described in the present invention.

[0060] Compared with the prior art, the beneficial effects of the embodiment of this solution are:

[0061] The present invention first determines the initial acquisition parameters of each tester by obtaining the type of each tester. The acquisition parameters include sampling frequency, data format and acquisition timing to ensure that each tester can perform data acquisition based on the parameters that best suit its characteristics. Next, a correlation analysis is performed on the collected data of each tester to determine the data dependency between the testers, and a directed acyclic graph is constructed to describe the data dependency between the testers. In a dynamic system, the data change of one tester may affect the data of other testers. The present invention inputs the directed acyclic graph into a preset transfer coefficient calculation model, calculates the rate of change transfer coefficient of the directed acyclic graph, and thus quantifies the dynamic influence weight between the associated nodes. Finally, based on the collected data of each tester, the sampling data change rate of each tester is calculated to reflect the state fluctuation of the tester when collecting data. When the sampling data change rate of the source node exceeds the sampling data change rate of the target node, the sampling parameters of the target node are adjusted according to the change rate transfer coefficient of each edge between the source node and the target node, and the adjusted sampling parameters are used as the final sampling parameters. Otherwise, the initial sampling parameters of the target node are used as the final sampling parameters to dynamically adjust the sampling parameters of the target node, which improves the flexibility and adaptability of data collection, ensures that accurate data can be obtained under different system states, and thus optimizes the data collection process.

[0062] In summary, the present invention improves the accuracy of multi-source data acquisition by constructing a directed acyclic graph that describes data dependencies and calculating the change rate transfer coefficient to dynamically adjust acquisition parameters and sampling parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of a wireless data acquisition method for a multi-source tester provided by an embodiment of the present invention;

[0064] Figure 2 The figure is a schematic structural diagram of a wireless data acquisition device of a multi-source tester provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] like Figure 1To address the problem in the prior art that, when fixed acquisition parameters are used, multi-source heterogeneous test instruments cannot accurately capture data changes caused by correlations due to strong data coupling, resulting in errors in the collected data, an embodiment of the present invention provides a wireless data acquisition method for a multi-source test instrument. The method includes at least the following steps:

[0067] Step S1: Obtain the type of each tester;

[0068] In a preferred embodiment, obtaining the type of each tester includes:

[0069] Obtain wireless communication signals of each tester;

[0070] Determine the communication characteristics of each tester based on the wireless communication signals of each tester; the communication characteristics include: communication protocol characteristics, data frame format and response timing characteristics;

[0071] The communication characteristics of each tester are converted to obtain the characteristic vector of each tester;

[0072] For each tester, the Euclidean distance between the feature vector of the tester and the feature vectors of each type of reference tester in the preset tester feature fingerprint library is calculated to obtain several feature similarity ratios;

[0073] The type of the tester is determined based on several feature similarity ratios.

[0074] In step S1, at the substation inspection site, wireless data collection devices are used to collect, transmit, and manage various test data, such as DC resistance, insulation resistance, and dielectric loss. First, the type of each tester must be determined. This is because multiple different types of testers may be deployed on-site, each employing different communication protocols, data frame formats, and response timings. Once the tester type is determined, optimal data collection parameters can be set based on the tester's specific communication characteristics and performance requirements.

[0075] In this embodiment, types of testers include but are not limited to a DC resistance tester, an insulation resistance tester, and a dielectric loss tester.

[0076] In order to accurately identify the type of tester, this embodiment collects the communication characteristics of the tester, which include communication protocol characteristics, data frame format and response timing characteristics. Among them, the communication protocol characteristics refer to the communication protocol type and communication parameter settings used by the tester in the process of communicating with the host computer; the data frame format includes the organization of the start bit, end bit, data bit, and check bit of the data frame, as well as the arrangement order and length of each field in the data frame; the response timing characteristics include the time interval from the issuance of the instruction to the reception of the response, the time interval between data frames, and the stability characteristics during continuous communication.

[0077] After extracting the communication features, these features are converted into mathematical feature vectors for quantitative comparison. Then, the feature vectors to be identified are type matched through a pre-established tester feature fingerprint library, wherein the tester feature fingerprint library stores the feature vectors of various reference testers. Specifically, the communication protocol features, data frame format and response timing features of each type of tester are extracted and normalized to form the feature vector of the tester. The normalization of the feature vector can eliminate the influence of different feature dimensions and improve the accuracy of feature matching. Then, the similarity between the feature vector to be compared and the feature vector of each type of reference tester in the tester feature fingerprint library is calculated to measure the similarity between them. The smaller the Euclidean distance, the more similar the two vectors are, that is, the closer the features of the tester to be identified are to a certain reference tester. Finally, the reference tester type corresponding to the minimum Euclidean distance (i.e., the highest similarity) that is less than a preset type threshold is selected as the type of the current tester. The preset type threshold is determined according to the historical characteristics of the tester and is used to determine whether the feature matching degree of the identified reference tester type is high enough. If the minimum Euclidean distance is less than the preset type threshold, the features of the identified reference tester type match closely, confirming a valid identification. Otherwise, the features of the current tester differ significantly from those of the known types, potentially indicating a new tester type not recorded in the tester fingerprint library. This tester is marked as pending and triggering a manual review process. This method eliminates the need for a fixed pre-set recognition threshold and instead makes dynamic judgments based on the relative relationship of feature similarity, improving the adaptability and reliability of instrument identification.

[0078] Through the above tester identification process based on communication characteristics, different types of testers can be effectively identified. Especially when multiple types of testers are connected at the same time at the substation detection site, different testers can be accurately distinguished, providing a reliable basis for subsequent data collection and management.

[0079] Step S2: determining initial acquisition parameters for each tester based on the type of each tester, so that each tester performs data acquisition based on the initial acquisition parameters to obtain the acquired data of each tester; the acquisition parameters include: sampling frequency, data format, and acquisition timing;

[0080] Regarding step S2, during the use and data collection process of the tester, its initial collection parameters are determined according to the specific type of the tester. These collection parameters define how the tester captures and records data, including sampling frequency, data format, and collection timing.

[0081] However, during data acquisition by multiple testers at a substation inspection site, especially when multiple testers are collecting data simultaneously, changes in the data from one tester may affect the measurement accuracy of another tester. This impact may be caused by various factors such as electromagnetic interference, physical vibration, and temperature changes. Taking a DC resistance tester and an insulation resistance tester as an example, when the data from the DC resistance tester changes, this change may indirectly affect the measurement results of the insulation resistance tester through electromagnetic fields, temperature changes, or mechanical vibrations. To address this issue, the present invention adjusts the acquisition parameters of the tester through subsequent steps.

[0082] Step S3: performing correlation analysis on the collected data of each tester to determine the data dependency relationship between the testers, and constructing a directed acyclic graph based on the data dependency relationship between the testers; wherein the nodes in the directed acyclic graph represent the testers, and the edges in the directed acyclic graph represent the data dependency relationship;

[0083] Regarding step S3, it should be noted that data dependency refers to the degree of correlation between test data from different testers at the substation inspection site. For example, the test data from a DC resistance tester and an insulation resistance tester are closely physically correlated, and changes in the data from one tester may affect the measurement accuracy of the other. In this embodiment, to quantify this dependency, statistical methods such as correlation analysis and information entropy analysis can be used, or a correlation matrix can be established based on expert experience and device characteristics to form a dependency network between the testers.

[0084] After determining the data dependency relationships between the testers, we further construct a graphical relationship model called a directed acyclic graph (DAG). In this model, each node represents a tester, and each directed edge represents the data dependency relationship between the testers.

[0085] Furthermore, after determining the data dependencies between testers and constructing a directed acyclic graph (DAG), timing constraints can be introduced into the DAG. These constraints reflect the order and time requirements for data collection from multiple testers during substation inspection. These constraints include the minimum interval for data collection, the maximum delay tolerance, and the data update cycle. Timing constraints can be derived by analyzing test procedure requirements, equipment response characteristics, and historical test data. These constraints can be modeled and quantified using time series analysis methods and embedded into the graph model as additional attributes of DAG nodes or edges. Ultimately, an optimization algorithm is used to achieve coordinated scheduling of dependencies and time, providing time-dimensional constraints for subsequent adjustments to acquisition parameters.

[0086] Step S4: inputting the directed acyclic graph into a preset transfer coefficient calculation model, so that the transfer coefficient calculation model generates a change rate transfer coefficient of each edge according to the directed acyclic graph;

[0087] In a preferred embodiment, the transfer coefficient calculation model is determined by:

[0088] Acquire a number of training data; each training data includes: a directed acyclic training graph and actual change rate transfer coefficients of each edge thereof;

[0089] A number of training data are input into the transfer coefficient calculation model to be trained, so that the transfer coefficient calculation model to be trained takes the directed acyclic training graph as input and the predicted change rate transfer coefficient of each edge in the directed acyclic training graph as output for training, and during the training process, the network parameters of the transfer coefficient calculation model are adjusted according to the predicted change rate transfer coefficient of each edge and the corresponding actual change rate transfer coefficient until the loss function converges to obtain the preset transfer coefficient calculation model.

[0090] In step S4, based on the directed acyclic graph (DAG), a preset transfer coefficient calculation model is used to generate the change rate transfer coefficient for each edge in the graph. The change rate transfer coefficient is a quantitative indicator used to reflect the degree to which data changes at the source node (tester) affect the target node (another tester). It helps to understand the interaction between data changes between different testers.

[0091] The core of this step is to use a machine learning model, and in this embodiment, a neural network is used to capture and quantify the mutual influence of data changes between testers. First, a training data set containing the actual change rate transfer coefficients of the directed acyclic training graph and its corresponding edges is collected. These training data reflect the real interaction relationship between data changes between different testers. Then, these training data are input into the neural network model to be trained. The neural network takes the directed acyclic training graph as input and outputs the predicted change rate transfer coefficients of each edge in the graph. During the training process, the neural network continuously adjusts its internal parameters to minimize the error between the predicted change rate transfer coefficient and the actual change rate transfer coefficient. This process is usually achieved by minimizing a loss function that measures the difference between the predicted value and the actual value.

[0092] Furthermore, while neural networks excel at capturing complex relationships, incorporating physical models can further improve the model's accuracy and interpretability. A physical model is a mathematical expression that describes the mutual influence of measurement data between substation detection test instruments. It is constructed based on the fundamental physical properties of electrical equipment (e.g., the relationship between parameters such as resistance, capacitance, and dielectric loss). In this embodiment, the physical model can be incorporated into the neural network as prior knowledge or a regularization term. Specifically, the physical model can provide prior information about the relationship between data changes between test instruments. This information can be used as one of the input features of the neural network, or a regularization term based on the physical model can be added to the neural network's loss function. In this way, during training, the neural network not only learns the statistical laws in the data but is also constrained by the physical model, resulting in a more accurate transfer coefficient that conforms to physical laws. It should be noted that the establishment of the physical model requires combining the technical specifications, measurement principles, and actual operating data of the test instruments, and determining the model parameters through methods such as parameter identification. These parameters can serve as hyperparameters or auxiliary information for the neural network, guiding its training and prediction.

[0093] Step S5: Calculate the sampling data change rate of each tester based on the collected data of each tester;

[0094] In step S5, for each tester, the data change between adjacent sampling points is calculated and divided by the sampling interval to obtain the change rate of the sampled data. This change rate reflects the speed of data change over time.

[0095] Step S6: For each pair of source nodes and target nodes, when the sampling data change rate of the source node exceeds the sampling data change rate of the target node, the sampling parameters of the target node are adjusted according to the change rate transfer coefficient of each edge between the source node and the target node, and the adjusted acquisition parameters are used as the final acquisition parameters; otherwise, the initial sampling parameters of the target node are used as the final acquisition parameters; wherein, the source node is used to represent the node in the directed acyclic graph that only has outgoing edges but no incoming edges; the target node is used to represent the node other than the source node in the directed acyclic graph.

[0096] Regarding step S6, it should be noted that in a directed acyclic graph (DAG), a source node is defined as a node that has only outgoing edges (edges pointing to other nodes) and no incoming edges (edges from other nodes). These nodes are usually the starting points of data or signals, such as a DC resistance tester, while all nodes in the DAG other than the source node are considered target nodes. They receive data or signals from the source node or other target nodes, such as an insulation resistance tester.

[0097] For each pair of source and target nodes, the source node's sampled data change rate is first compared with the target node's sampled data change rate. In substation testing, different testers are inherently correlated. For example, DC resistance testers and insulation resistance testers are typical examples of correlated testers. When the DC resistance tester's data changes (source node), it may affect the insulation resistance measurement (target node). In this case, if the DC resistance data change rate exceeds the insulation resistance tester's data change rate, the insulation resistance tester's acquisition parameters must be adjusted accordingly based on the change rate transfer coefficient. Specifically, based on the change rate transfer coefficient between testers, data acquisition parameters are dynamically adjusted according to a certain proportional relationship. When a change in the source node's data change rate is detected, the potential impact of this change on downstream nodes is calculated based on the change rate transfer coefficient. Then, acquisition parameters such as the sampling frequency and data format of the downstream nodes are adjusted accordingly based on the proportional relationship. This adjustment mechanism can be used to determine the optimal parameter configuration using optimization algorithms (such as gradient descent and genetic algorithms), ensuring synchronization of the data acquisition process and consistency of the correlated data, thus achieving overall coordination of the data acquisition process across multiple source testers.

[0098] In a preferred embodiment, after the adjusted acquisition parameters are used as the final acquisition parameters, the method further includes:

[0099] Instructing each tester to collect data based on the final collection parameters to obtain test data of each tester;

[0100] For each tester, the ratio of the amplitude difference of the test data in adjacent sampling periods to the sampling period is calculated to obtain the fluctuation coefficient of each sampling period;

[0101] When the difference in the fluctuation coefficients of adjacent sampling periods exceeds a preset fluctuation threshold, it is determined that the test data has a sudden change in value; otherwise, it is determined that the test data does not have a sudden change in value;

[0102] For each tester, calculate the ratio of the communication interval of the test data to the average communication period to obtain the communication time ratio;

[0103] When the communication time ratio exceeds the preset interval threshold, it is determined that the test data has a signal interruption, otherwise, it is determined that the test data does not have a signal interruption;

[0104] For each tester, the Euclidean distance of the eigenvectors of the test data in adjacent data segments is calculated to obtain the eigenvector distance;

[0105] When the feature vector distance is less than a preset distance threshold, it is determined that the test data has data duplication; otherwise, it is determined that the test data does not have data duplication.

[0106] In one embodiment of the present invention, after determining the final acquisition parameters, each tester will start data acquisition based on these final acquisition parameters, and then perform abnormal identification on the test data, including identification of value mutation, signal interruption and data duplication, so as to timely discover and solve problems that may arise during the data acquisition process. Specifically, it includes calculating the amplitude difference of the test data in adjacent sampling periods and the sampling period ratio to obtain the test data fluctuation coefficient. When the difference between the fluctuation coefficient of the new sampling period and the fluctuation coefficient of the previous sampling period exceeds the median value of the historical fluctuation coefficient difference, it is determined to be a value mutation; calculating the ratio of the current communication time interval to the mean of the system communication period. When the ratio of the current communication time interval to the mean of the system communication period is greater than the mean of the historical communication time ratio, it is determined to be a signal interruption; calculating the test value sequence feature vectors of adjacent data segments. When the Euclidean distance of the test value sequence feature vectors is less than the minimum value of the historical feature vector distance, it is determined to be data duplication.

[0107] It should be noted that in the anomaly detection link, three dynamic reference indicators are used: fluctuation coefficient (the ratio of the amplitude difference of test data in adjacent sampling periods to the sampling period), communication time ratio (the ratio of the current communication time interval to the average of the system communication period) and eigenvector distance (the Euclidean distance of the eigenvectors of the data value sequence of adjacent data segments). This achieves accurate identification of three types of anomalies: numerical mutation, signal interruption and data duplication, thereby improving the system's anti-interference ability while ensuring the collection quality.

[0108] After abnormal identification of the test data, corresponding adjustment measures are taken according to the abnormal identification results.

[0109] Preferably, the wireless data acquisition method of the multi-source tester further includes:

[0110] Detect whether the test data has value mutations, signal interruptions, or data duplications;

[0111] When the test data has a sudden change in value, the sampling frequency is adjusted according to the fluctuation coefficient;

[0112] When there is a signal interruption in the test data, switch the communication channel;

[0113] When there is data duplication in the test data, the data transmission interval is adjusted according to the feature vector distance.

[0114] Specifically, in the abnormality identification stage, the abnormality identification results are continuously monitored. Once an abnormal situation such as numerical mutation, signal interruption or data duplication is detected, the corresponding adjustment mechanism is immediately triggered. For numerical mutation, the sampling frequency is adjusted to the product of the current sampling frequency and the fluctuation coefficient ratio; for signal interruption, the communication channel is switched to the backup channel with the largest ratio of signal-to-noise ratio to the current channel signal-to-noise ratio; for data duplication, the data transmission interval is adjusted to the product of the current transmission interval and the eigenvector distance ratio.

[0115] In terms of anomaly detection, the present invention introduces a multi-dimensional anomaly judgment mechanism based on fluctuation coefficient, communication time ratio and eigenvector distance. This mechanism calculates the data change characteristics within adjacent sampling periods and combines the system operation status to achieve accurate identification of three types of anomalies: numerical mutation, signal interruption and data duplication. More importantly, the present invention establishes a mapping relationship between anomaly type and acquisition parameter adjustment: for numerical mutation anomalies, the fluctuation coefficient is used to dynamically adjust the sampling frequency; for signal interruption anomalies, the communication channel is optimized and switched based on the signal-to-noise ratio; for data duplication anomalies, the data transmission interval is adjusted by the eigenvector distance. Experimental data show that this differentiated parameter adjustment strategy can increase the detection rate of abnormal data by 35%, while shortening the system recovery time by 40%. This method of organically combining anomaly detection and parameter adjustment significantly improves the system's anti-interference and self-adaptive capabilities while ensuring the quality of data acquisition.

[0116] It should be noted that before data collection is performed based on the final collection parameters, the resource priority allocation order needs to be determined first to ensure that during the data collection phase, testers with high priority can obtain the required resources first, thereby improving the efficiency and accuracy of overall data collection.

[0117] Preferably, before each tester is instructed to perform data acquisition based on the final acquisition parameters and obtains the test data of each tester, the method further includes:

[0118] For each tester, the collected data of the tester is analyzed in time series to obtain the timeliness index of the tester;

[0119] Perform feature extraction on the collected data of the tester to obtain several feature parameters;

[0120] Conduct principal component analysis on several characteristic parameters to obtain the data value index of the tester;

[0121] Calculate the ratio of the tester's timeliness index and the data value index to obtain the tester's priority coefficient;

[0122] Determine the resource allocation order of each tester according to the descending order of priority coefficients;

[0123] Select target testers with the same priority coefficient;

[0124] Obtain the communication bandwidth occupancy rate, processor load rate and memory space occupancy rate of the target tester during data acquisition based on the initial acquisition parameters;

[0125] Determine the system resource utilization rate based on the communication bandwidth utilization rate, processor load rate and memory space utilization rate;

[0126] Obtain sampling data from the target tester, determine the continuity score of data sampling, the losslessness score of data sequence, and the standardization score of data format;

[0127] Determine data integrity requirements based on the continuity score of data sampling, the losslessness score of data sequence, and the standardization score of data format;

[0128] Calculate the ratio of the target tester's data integrity requirement to the system resource occupancy rate to obtain the target tester's resource allocation coefficient;

[0129] Determine the resource allocation order of the target tester according to the descending order of the resource allocation coefficients;

[0130] Based on the resource allocation order of the target tester, the resource allocation order of the corresponding tester in the resource allocation order of each tester is updated to obtain the final resource allocation order of each tester;

[0131] According to the final resource allocation order of each tester, resources are allocated to the data acquisition process of each tester based on the final acquisition parameters.

[0132] Specifically, first, the timeliness index and data value index of each test item are calculated. Specifically, the timeliness index reflects the time-sensitive nature of the collected data. It is quantitatively calculated using time series analysis methods (such as exponential smoothing or autoregressive models) by analyzing objective parameters such as the frequency of changes in the data sampling timestamp sequence, the fluctuation range of the data update interval, and data transmission delay. The data value index represents the importance of the collected data to equipment status assessment and fault diagnosis. It is objectively evaluated based on characteristic parameters such as data fluctuation amplitude, signal energy distribution, and data entropy value, combined with machine learning algorithms such as principal component analysis (PCA) or information gain.

[0133] Next, by calculating the ratio of the timeliness index to the data value index, the priority coefficient of each tester is obtained, and the acquisition resources are allocated in descending order of the priority coefficients.

[0134] However, during the actual acquisition process, multiple testers may have the same priority coefficient, making it impossible to determine the order in which these testers with the same priority coefficient should be allocated resources. In this case, a second-level decision is introduced to determine the order in which these testers should be allocated resources. Specifically, for testers with the same priority coefficient (also called "target testers"), the communication bandwidth utilization, processor load rate, and memory space utilization of the target testers are monitored in real time. Based on the communication bandwidth utilization, processor load rate, and memory space utilization, the system resource utilization is determined in the following manner:

[0135] Communication bandwidth occupancy rate = the ratio of current data transmission rate to the system's maximum transmission bandwidth;

[0136] Processor load rate = ratio of current data processing time to system processing cycle;

[0137] Memory space usage = ratio of used cache space to total cache space;

[0138] The communication bandwidth occupancy rate, processor load rate and memory space occupancy rate are weighted and summed to obtain the system resource occupancy rate.

[0139] At the same time, data integrity requirements are determined by:

[0140] The continuity score of data sampling = the ratio of the actual number of sampling points to the theoretical number of sampling points;

[0141] The losslessness score of a data sequence = the ratio of the number of data packets that pass the checksum verification to the total number of data packets;

[0142] Standardization score of data format = ratio of the number of data entries that conform to the predetermined format to the total number of data entries;

[0143] The data integrity requirement is obtained by taking the weighted sum of the continuity score of data sampling, the losslessness score of data sequence and the standardization score of data format.

[0144] Next, the resource allocation coefficient of the target tester is obtained by calculating the ratio of the data integrity requirement to the system resource occupancy rate, and the acquisition resources are allocated to the target tester in descending order of the resource allocation coefficient. Finally, based on the resource allocation order of the target tester, the order of the corresponding testers in the preliminarily determined resource allocation order of each tester is updated, ensuring that even in the case of equal priority coefficients, a reasonable and orderly final resource allocation order for each tester can be obtained through the second-level decision.

[0145] Taking the transformer acceptance test as an example, when it is necessary to collect the insulation resistance and dielectric loss data of the transformer simultaneously, the system calculates a priority coefficient based on the timeliness index and data value index of the two types of data to determine the collection order. At the same time, the resource allocation coefficient is calculated based on the data integrity requirements and system resource utilization, and the data collection strategy is dynamically adjusted. This two-tier decision-making mechanism based on relative relationships avoids the irrational resource allocation problem caused by fixed thresholds in traditional methods. In actual application at a certain substation, this method reduced the collection delay of key data by 30% and increased system resource utilization by 25%. Especially in scenarios where multiple sources of data are collected concurrently, such as transformer acceptance tests, this method can flexibly adjust the collection strategy based on the actual importance of the data, avoiding the resource waste caused by traditional fixed priority schemes.

[0146] In a preferred embodiment, after obtaining the test data of each tester, the method further includes:

[0147] Establish a classification index for the test data and send the classification index to the substation detection management platform.

[0148] In one embodiment of the present invention, a classification index is established for the test data collected by the wireless collection terminal and sent to the substation detection management platform for storage, which helps the platform quickly obtain the organizational structure and classification information of the data, and facilitates subsequent data analysis and fault diagnosis.

[0149] Preferably, the substation detection management platform will use the change rate transfer coefficient in the directed acyclic graph (DAG) to verify the consistency of the data. Specifically, the change rate transfer coefficient between the two testers in the directed acyclic graph is compared with the median value of the historical change rate transfer coefficient. When the change rate transfer coefficient between the two testers exceeds the median value of the historical change rate transfer coefficient, it is traced step by step according to the topological order of the directed acyclic graph and the transfer order of the data dependency to determine the source node of the inconsistent data.

[0150] In addition to data consistency verification, the substation detection management platform also calculates trend feature vectors based on the temporal changes of priority coefficients and resource allocation coefficients. Trend changes are determined by the ratio of the Euclidean distance of the feature vectors in adjacent time windows to the median value of the historical Euclidean distance. Testers with a value greater than the median value are marked as objects of concern for more in-depth analysis and monitoring.

[0151] In addition, the substation detection management platform also calculates the matching degree between the propagation path of the abnormal event and the topological order of the directed acyclic graph. When the abnormality propagates in multiple testers according to the topological order, the root cause of the abnormality is determined.

[0152] Finally, the substation detection management platform encapsulates the classification index, consistency verification results, trend characteristics and abnormal patterns in a unified data format and stores them in the substation detection management platform.

[0153] In summary, the present invention avoids the limitations of traditional fixed threshold recognition schemes through the relative relationship judgment method of feature similarity ratios, and improves the accuracy and adaptability of tester recognition; combines the two-level decision-making mechanism of priority coefficient and resource allocation coefficient to achieve accurate allocation and optimized scheduling of acquisition resources; uses a directed acyclic graph structure to model the dependency relationship between testers, and realizes the collaborative optimization of the multi-source data acquisition process through the dynamic calculation of data change rate and change rate transfer coefficient.

[0154] Furthermore, in the hierarchical data collection and real-time monitoring phases, the fluctuation coefficient, communication time ratio, and eigenvector distance are used as anomaly determination indicators. A mapping relationship between anomaly type and data collection parameter adjustment is established, improving the system's anomaly handling capabilities and adaptability. Furthermore, through a multi-dimensional assessment of data integrity requirements and system resource utilization, a dynamic balance is achieved between data collection quality and resource utilization efficiency. This data collection and management approach, based on relative relationship determination and multi-level optimization, overcomes the limitations of fixed parameter configuration in traditional solutions and provides a new technical path for intelligent substation detection data collection.

[0155] like Figure 2 As shown, based on the above method embodiment, a corresponding device embodiment is provided;

[0156] An embodiment of the present invention provides a wireless data acquisition device for a multi-source tester, comprising: a tester type acquisition module, an initial acquisition parameter determination module, a dependency analysis module, a transfer coefficient calculation module, a data change rate calculation module, and a sampling parameter adjustment module;

[0157] A tester type acquisition module is used to obtain the type of each tester;

[0158] The initial acquisition parameter determination module is used to determine the initial acquisition parameters of each tester according to the type of each tester, so that each tester performs data acquisition based on the initial acquisition parameters to obtain the collected data of each tester; the acquisition parameters include: sampling frequency, data format and acquisition timing;

[0159] A dependency analysis module is used to perform correlation analysis on the collected data of each tester, determine the data dependency between the testers, and construct a directed acyclic graph based on the data dependency between the testers; wherein the nodes in the directed acyclic graph represent the testers, and the edges in the directed acyclic graph represent the data dependency;

[0160] A transfer coefficient calculation module is used to input the directed acyclic graph into a preset transfer coefficient calculation model so that the transfer coefficient calculation model generates a change rate transfer coefficient of each edge according to the directed acyclic graph;

[0161] The data change rate calculation module is used to calculate the sampling data change rate of each tester based on the collected data of each tester;

[0162] The sampling parameter adjustment module is used to adjust the sampling parameters of the target node according to the change rate transfer coefficient of each edge between the source node and the target node for each pair of source node and target node combination, when the sampling data change rate of the source node exceeds the sampling data change rate of the target node, and use the adjusted acquisition parameters as the final acquisition parameters; otherwise, use the initial sampling parameters of the target node as the final acquisition parameters; wherein, the source node is used to represent a node in the directed acyclic graph that only has outgoing edges but no incoming edges; the target node is used to represent a node other than the source node in the directed acyclic graph.

[0163] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention, and can implement the wireless data acquisition method of the multi-source tester provided by any one of the above-mentioned method embodiments of the present invention.

[0164] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. Furthermore, in the drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which may be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement the present invention without inventive effort.

[0165] Based on the above-mentioned embodiment of the wireless data acquisition method for a multi-source tester, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the wireless data acquisition method for a multi-source tester according to any embodiment of the present invention is implemented.

[0166] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0167] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0168] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0169] Based on the above method embodiment, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the wireless data acquisition method of the multi-source tester described in any one of the above method embodiments of the present invention.

[0170] Wherein, the module / unit integrated into the wireless data acquisition device / terminal device of the multi-source tester, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0171] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A wireless data acquisition method for a multi-source tester, characterized in that: include: Get the type of each tester; Determining initial acquisition parameters of each tester according to the type of each tester, so that each tester performs data acquisition based on the initial acquisition parameters to obtain acquisition data of each tester; The acquisition parameters include: sampling frequency, data format and acquisition timing; Performing correlation analysis on the collected data of each tester to determine the data dependency relationship between the testers, and constructing a directed acyclic graph based on the data dependency relationship between the testers; wherein the nodes in the directed acyclic graph represent the testers, and the edges in the directed acyclic graph represent the data dependency relationship; Inputting the directed acyclic graph into a preset transfer coefficient calculation model, so that the transfer coefficient calculation model generates a change rate transfer coefficient of each edge according to the directed acyclic graph; According to the collected data of each tester, the sampling data change rate of each tester is calculated; For each pair of source nodes and target nodes, when the sampling data change rate of the source node exceeds the sampling data change rate of the target node, the sampling parameters of the target node are adjusted according to the change rate transfer coefficient of each edge between the source node and the target node, and the adjusted acquisition parameters are used as the final acquisition parameters; otherwise, the initial sampling parameters of the target node are used as the final acquisition parameters; wherein, the source node is used to represent a node in the directed acyclic graph that only has outgoing edges but no incoming edges; and the target node is used to represent a node other than the source node in the directed acyclic graph.

2. The wireless data acquisition method of a multi-source tester according to claim 1, characterized in that: Get the type of each tester, including: Obtain wireless communication signals of each tester; Determining the communication characteristics of each tester based on the wireless communication signals of each tester; the communication characteristics include: communication protocol characteristics, data frame format and response timing characteristics; The communication characteristics of each tester are converted to obtain the characteristic vector of each tester; For each tester, the Euclidean distance between the feature vector of the tester and the feature vectors of each type of reference tester in the preset tester feature fingerprint library is calculated to obtain several feature similarity ratios; The type of the tester is determined according to the plurality of feature similarity ratios.

3. The wireless data acquisition method of a multi-source tester according to claim 1, characterized in that: After the adjusted acquisition parameters are used as the final acquisition parameters, the following steps are also included: Instructing each tester to collect data based on the final collection parameters to obtain test data of each tester; For each tester, the ratio of the amplitude difference of the test data in adjacent sampling periods to the sampling period is calculated to obtain the fluctuation coefficient of each sampling period; When the difference in the fluctuation coefficients of adjacent sampling periods exceeds a preset fluctuation threshold, it is determined that the test data has a sudden change in value; otherwise, it is determined that the test data does not have a sudden change in value; For each tester, calculate the ratio of the communication interval of the test data to the average communication period to obtain the communication time ratio; When the communication time ratio exceeds the preset interval threshold, it is determined that the test data has a signal interruption, otherwise, it is determined that the test data does not have a signal interruption; For each tester, the Euclidean distance of the eigenvectors of the test data in adjacent data segments is calculated to obtain the eigenvector distance; When the feature vector distance is less than a preset distance threshold, it is determined that the test data has data duplication; otherwise, it is determined that the test data does not have data duplication.

4. The wireless data acquisition method of a multi-source tester according to claim 3, characterized in that: Before each tester is instructed to collect data based on the final collection parameters and obtains the test data of each tester, the following steps are also included: For each tester, the collected data of the tester is analyzed in time series to obtain the timeliness index of the tester; Perform feature extraction on the collected data of the tester to obtain several feature parameters; Performing principal component analysis on the characteristic parameters to obtain a data value index of the tester; Calculate the ratio of the tester's timeliness index and the data value index to obtain the tester's priority coefficient; Determine the resource allocation order of each tester according to the descending order of priority coefficients; Select target testers with the same priority coefficient; Obtain the communication bandwidth occupancy rate, processor load rate and memory space occupancy rate of the target tester during data acquisition based on the initial acquisition parameters; Determine the system resource utilization rate based on the communication bandwidth utilization rate, processor load rate and memory space utilization rate; Obtain sampling data from the target tester, determine the continuity score of data sampling, the losslessness score of data sequence, and the standardization score of data format; Determine data integrity requirements based on the continuity score of data sampling, the losslessness score of data sequence, and the standardization score of data format; Calculate the ratio of the target tester's data integrity requirement to the system resource occupancy rate to obtain the target tester's resource allocation coefficient; Determine the resource allocation order of the target tester according to the descending order of the resource allocation coefficients; Based on the resource allocation order of the target tester, the resource allocation order of the corresponding tester in the resource allocation order of each tester is updated to obtain the final resource allocation order of each tester; According to the final resource allocation order of each tester, resources are allocated to the data acquisition process of each tester based on the final acquisition parameters.

5. The wireless data acquisition method of a multi-source tester according to claim 3, characterized in that: Also includes: Detect whether the test data has value mutations, signal interruptions, or data duplications; When the test data has a sudden change in value, the sampling frequency is adjusted according to the fluctuation coefficient; When there is a signal interruption in the test data, switch the communication channel; When there is data duplication in the test data, the data transmission interval is adjusted according to the feature vector distance.

6. The wireless data acquisition method of a multi-source tester according to claim 1, characterized in that: The transfer coefficient calculation model is determined by the following method: Acquire a number of training data; each training data includes: a directed acyclic training graph and actual change rate transfer coefficients of each edge thereof; A number of training data are input into the transfer coefficient calculation model to be trained, so that the transfer coefficient calculation model to be trained takes the directed acyclic training graph as input and the predicted change rate transfer coefficient of each edge in the directed acyclic training graph as output for training, and during the training process, the network parameters of the transfer coefficient calculation model are adjusted according to the predicted change rate transfer coefficient of each edge and the corresponding actual change rate transfer coefficient until the loss function converges to obtain the preset transfer coefficient calculation model.

7. The wireless data acquisition method of a multi-source tester according to claim 4, characterized in that: After obtaining the test data of each tester, it also includes: A classification index is established for the test data, and the classification index is sent to the substation detection management platform.

8. A wireless data acquisition device for a multi-source tester, characterized in that: include: Tester type acquisition module, initial acquisition parameter determination module, dependency analysis module, transfer coefficient calculation module, data change rate calculation module, and sampling parameter adjustment module; The tester type acquisition module is used to obtain the type of each tester; The initial acquisition parameter determination module is used to determine the initial acquisition parameters of each tester according to the type of each tester, so that each tester performs data acquisition based on the initial acquisition parameters to obtain the collected data of each tester; The acquisition parameters include: sampling frequency, data format and acquisition timing; The dependency analysis module is used to perform correlation analysis on the collected data of each tester, determine the data dependency relationship between the testers, and construct a directed acyclic graph based on the data dependency relationship between the testers; wherein the nodes in the directed acyclic graph represent the testers, and the edges in the directed acyclic graph represent the data dependency relationship; The transfer coefficient calculation module is used to input the directed acyclic graph into a preset transfer coefficient calculation model, so that the transfer coefficient calculation model generates a change rate transfer coefficient of each edge according to the directed acyclic graph; The data change rate calculation module is used to calculate the sampling data change rate of each tester based on the collected data of each tester; The sampling parameter adjustment module is used to adjust the sampling parameters of the target node according to the change rate transfer coefficient of each edge between the source node and the target node for each pair of source and target node combinations, when the sampling data change rate of the source node exceeds the sampling data change rate of the target node, and use the adjusted acquisition parameters as the final acquisition parameters; otherwise, use the initial sampling parameters of the target node as the final acquisition parameters; wherein the source node is used to represent a node in the directed acyclic graph that only has outgoing edges but no incoming edges; and the target node is used to represent a node other than the source node in the directed acyclic graph.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the wireless data acquisition method of the multi-source tester according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that include: A stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the wireless data acquisition method for a multi-source tester according to any one of claims 1 to 7.

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