HPLC and HRF dual-mode communication data intelligent acquisition method and system

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent acquisition method and system for HPLC (High Performance Liquid Chromatography) and HRF (High Frequency) dual-mode communication data. The method comprises the following steps: constructing an isolated tree according to multi-dimensional data points of power data; calculating the ratio of the total number of the multi-dimensional data points of the node where any multi-dimensional data point is located in any isolated tree to the total number of the multi-dimensional data points of the father node of the multi-dimensional data point; calculating the abnormal degree of the dimension according to the ratio; for any one multi-dimensional data point, the noise degree is calculated according to the abnormal degree of any dimension of the multi-dimensional data point; and calculating a filtering window of the multi-dimensional data points according to the noise degrees of all the multi-dimensional data points in the local range of the multi-dimensional data points, and smoothing the power data according to the filtering window to obtain optimal power data, thereby completing power data acquisition. The smoothing processing result of the electric power data is more accurate.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for intelligent acquisition of dual-mode communication data using HPLC and HRF. Background Technology

[0002] In the dual-mode communication architecture of HPLC (High-speed Power Line Communication) and HRF (High-frequency Radio Frequency), HPLC and HRF technologies are widely used in smart meters, distribution automation terminals, and energy management systems due to their complementary advantages. HPLC utilizes existing power lines for high-speed data transmission, offering advantages such as low deployment cost and wide coverage; while HRF enables flexible networking through wireless channels, making it suitable for areas with severe power line signal attenuation or complex topologies.

[0003] Electromagnetic noise, harmonics generated by nonlinear loads, switching transients, and internal circuit coupling in the power grid environment can distort raw measurements such as voltage and current, affecting the accuracy of parameters like electricity consumption, power, and harmonic content. This severely impacts the accuracy of subsequent data analysis, condition monitoring, and fault diagnosis. Because this noise is embedded at the data acquisition source, even if subsequent communication is completely reliable, the uploaded data will still be contaminated. Therefore, smoothing of meter data is necessary.

[0004] For example, the patent application document "Anti-electricity theft analysis method based on intelligent measurement terminal" with invention publication number "CN116008714A" points out that the collected electricity data is smoothed by using a mean filtering algorithm through a preset fixed-size filter window. However, since the electricity data is easily affected by noise factors during collection, the fixed-size filter window cannot handle the collected noise data points well. If the filter window is too large, it will over-filter the normal data, and if the filter window is too small, the collected data will be greatly affected by noise data, resulting in inaccurate smoothing results. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for intelligent acquisition of dual-mode communication data using HPLC and HRF, in order to solve the technical problem of inaccurate smoothing processing of power data.

[0006] In a first aspect, the present invention provides a method for intelligent acquisition of dual-mode communication data using HPLC and HRF, employing the following technical solution: Using a dual-mode communication architecture of HPLC and HRF, multi-dimensional data points containing voltage, current and power are collected from the power network as initial power data. An adaptive filtering window is set based on the initial power data, and the initial power data is smoothed according to the filtering window to obtain the optimal power data. The optimal power data is uploaded to the concentrator through dual channels of HPLC and HRF. The concentrator performs deduplication processing on the optimal power data transmitted through dual channels and uploads it to the power consumption information collection master station to complete the power data collection. The method for setting the filter window is as follows: Construct an isolated tree based on the initial power data; for any dimension of each multidimensional data point, calculate the ratio of the total number of multidimensional data points in the nodes of each level of the isolated tree to the total number of multidimensional data points in the parent node of the node to which the multidimensional data point belongs; calculate the degree of anomaly of that dimension based on the ratio. For each multidimensional data point, calculate the difference between the anomaly degree of any dimension of the multidimensional data point and the mean anomaly degree of all dimensions; calculate the noise level of the multidimensional data point based on the absolute value of the difference and the standard deviation of the anomaly degree of all dimensions of the multidimensional data point. The size of the filtering window for a multidimensional data point is calculated based on the noise level of all multidimensional data points within a local range of that multidimensional data point.

[0007] This invention calculates the degree of anomaly of any multidimensional data point in any dimension, calculates the noise level of the multidimensional data point based on the difference in the degree of anomaly of the multidimensional data point across all dimensions, calculates the size of the filtering window for the multidimensional data point based on the magnitude of the noise level, and performs smoothing processing on the multidimensional data point, thereby making the smoothing processing result more accurate.

[0008] Preferably, the step of constructing an isolated tree based on the initial power data includes the following steps: All multidimensional data points are used as segmentation samples for the Isolation Forest algorithm to construct an isolation tree. The segmentation dimension of each layer in the same isolation tree is the same, that is, the same isolation tree is constructed using data of the same dimension.

[0009] Constructing an isolation tree based on the same dimension can make full use of the distribution characteristics of the data in that dimension, and more quickly separate the data that differs significantly from other data in that dimension, thus making the calculation results of the anomaly degree of multidimensional data points more accurate.

[0010] Preferably, the degree of abnormality satisfies the expression: ; Indicates the first The first multidimensional data point The degree of anomaly in each dimension; Indicates the first The total number of isolated trees constructed by the dimension; Represents the ordinal number of an isolated tree; Indicates the first The multidimensional data points at the th The deepest level in an isolated tree; Represents the ordinal number of the level of an isolated tree; Indicates the first The multidimensional data points at the th The first of the isolated trees The total number of multidimensional data points in the nodes to which the layer belongs; Indicates the first The multidimensional data points at the th The first of the isolated trees The total number of multidimensional data points in the parent node of the node belonging to the layer, which represents the number of data points in the layer. The multidimensional data points at the th The first of the isolated trees The total number of multidimensional data points in the nodes to which the layer belongs; Represented by natural constant An exponential function with base 1.

[0011] The degree of anomaly of a data point is calculated by examining the changes in the number of all multidimensional data points in its node during the continuous segmentation of the isolated tree, thus making the calculation results more accurate.

[0012] Preferably, the method for obtaining the associated node is as follows: For any multidimensional data point, if all multidimensional data points in any node of the isolated tree contain the multidimensional data point, then the node is recorded as the target node of the multidimensional data point. The target node is selected from all nodes at each level of the isolated tree and is used as the node to which the multidimensional data point belongs at that level of the isolated tree.

[0013] Preferably, the noise level satisfies the expression: ; In the formula, Indicates the first The noise level of each multidimensional data point; Indicates the total number of dimensions; Indicates the ordinal number of the dimension; Indicates the first The first multidimensional data point The degree of anomaly in each dimension; Indicates the first The mean anomaly degree of all dimensions of a multidimensional data point; Indicates the absolute value symbol; This represents the standard normalization function.

[0014] By analyzing the differences in the degree of anomaly across all dimensions of any multidimensional data point, the noise level of that multidimensional data point is calculated, thus making the noise level calculation result more accurate.

[0015] Preferably, the local range of the multidimensional data points includes: For any multidimensional data point, the one closest to the time of its acquisition will be... Each multidimensional data point is considered as a local range of that multidimensional data point.

[0016] Preferably, the size of the filtering window satisfies the expression: ; In the formula, Indicates the first The size of the filtering window for each multidimensional data point; This indicates the size of the preset initial filtering window; Indicates the first The mean noise level of a multidimensional data point and all multidimensional data points within its local range; This represents the function for rounding up.

[0017] By analyzing the noise levels of different multidimensional data points, filter windows of different sizes are set for each data point. This allows the filtering of multidimensional data points to take into account the noise level, making the filter window larger for multidimensional data points with higher noise levels and smaller for multidimensional data points with lower noise levels, thereby making the filtering results more accurate.

[0018] Secondly, this invention provides an intelligent data acquisition system for dual-mode communication of HPLC and HRF, employing the following technical solution: A dual-mode HPLC and HRF communication data intelligent acquisition system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the aforementioned dual-mode HPLC and HRF communication data intelligent acquisition method.

[0019] By adopting the above technical solution, a computer program is generated from the above-mentioned intelligent acquisition method of HPLC and HRF dual-mode communication data and stored in the memory so that it can be loaded and executed by the processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0020] The present invention has the following technical effects: This invention calculates the noise level of any multidimensional data point by assessing its anomaly across all dimensions, and then calculates the filter window size for that data point based on the noise level, thereby making the smoothing results of power data more accurate.

[0021] Furthermore, this invention calculates the anomaly degree of a multidimensional data point by calculating the change in the total number of all multidimensional data points of its node during the continuous segmentation of an isolated tree in any dimension of any multidimensional data point. The more anomalous the multidimensional data point, the greater the change in the total number of all multidimensional data points of its node during the continuous segmentation process. This makes the calculation result of the anomaly degree of the multidimensional data point in that dimension more accurate, and further makes the calculation result of the noise level of the data point more accurate, thereby making the smoothing result of the power data more accurate. Attached Figure Description

[0022] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts.

[0023] Figure 1 This is a flowchart of a method for intelligent acquisition of dual-mode communication data using HPLC and HRF, according to an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It should be understood that when the terms "first," "second," etc., are used in the claims, specification, and drawings of this invention, they are only used to distinguish different objects and not to describe a specific order. The terms "comprising" and "including" used in the specification and claims of this invention indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0026] This invention discloses an intelligent data acquisition method for dual-mode communication using HPLC and HRF, referring to... Figure 1 This includes steps S1-S5: S1: Collect multi-dimensional data points of power data and construct an isolated tree.

[0027] It should be noted that because HPLC is susceptible to electric field pulse noise and harmonic interference, and HRF is susceptible to electromagnetic interference or multipath fading, the raw power data (especially continuous quantities such as voltage, current, and power) may contain abnormal jumps, glitches, or missing values. Therefore, this invention requires analysis of the initial power data collected.

[0028] By monitoring power data, potential faults or anomalies in the power system can be detected in a timely manner, thereby enabling preventive measures to be taken to reduce the risk of power outages and equipment damage.

[0029] In one embodiment, the metering chip inside the smart meter samples analog signals such as voltage, current, and power in real time. After conversion by an analog-to-digital converter (ADC) and digital signal processing, various types of power data are acquired and stored in a local register. The dimensions of the power data include voltage, current, and power. The invention pre-sets the data acquisition period to 1 second, which can be adjusted by implementers according to actual implementation needs.

[0030] All multidimensional data points are used as segmentation samples in the Isolation Forest algorithm to construct an isolation tree. Within the same isolation tree, each level has the same segmentation dimension; that is, the same isolation tree is constructed using data from the same dimension.

[0031] It's important to note that the Isolation Forest algorithm is a machine learning algorithm used for anomaly detection. Its core idea is to identify outliers in a dataset by constructing isolated trees. While each isolated tree is built independently, the algorithm enhances model stability and detection capabilities by constructing multiple isolated trees.

[0032] This completes the construction of the isolated tree based on the power data.

[0033] S2: Calculate the degree of anomaly of any multidimensional data point in any dimension.

[0034] It should be noted that, because power systems require continuous monitoring to ensure stable operation, monitoring power data can promptly detect potential system faults or anomalies, enabling rapid response and handling. However, the Isolation Forest algorithm calculates the anomaly level of a data point based on its distance from the root node; data points closer to the root node have a higher anomaly level. But this algorithm does not consider the changes in the number of samples in the node to which the data point belongs during the isolation tree segmentation process, leading to inaccurate anomaly level calculations. Therefore, this invention calculates the anomaly level of a multidimensional data point by analyzing the changes in the number of multidimensional data points in the node to which the data point belongs during the isolation tree segmentation process.

[0035] It should be further noted that when using the isolated forest algorithm to detect anomalies in multidimensional data points, the more anomalous the data point, the easier it is to isolate it. Therefore, the difference between anomalous data and normal data is relatively large. This means that for anomalous data points, the number of multidimensional data points in the nodes to which they belong will change significantly during the segmentation process using the isolated tree. Therefore, this invention calculates the degree of anomalousness of a data point by calculating the change in the number of multidimensional data points in the nodes to which it belongs during the segmentation process using the isolated tree.

[0036] In one embodiment, for any multidimensional data point, if all multidimensional data points in any node of the isolated tree contain that multidimensional data point, then that node is designated as the target node of that multidimensional data point. The target node is then selected from all nodes at each level of the isolated tree and designated as the node to which the multidimensional data point belongs at that level of the isolated tree. Specifically, during anomaly detection of initial power data using the isolated forest algorithm, all acquired initial power data is used as the root node of the isolated tree. A random threshold is set to divide the root node, resulting in two child nodes. For any multidimensional data point, it must exist in some child node; this child node is then designated as the node to which the multidimensional data point belongs at that level of the isolated tree. This process continues until the multidimensional data point is completely separated, thus obtaining the node to which the multidimensional data point belongs at each level of the isolated tree.

[0037] The formula for calculating the anomaly level of any dimension of any multidimensional data point is as follows: ; Indicates the first The first multidimensional data point The degree of anomaly in each dimension; Indicates the first The total number of isolated trees constructed by the dimension; Represents the ordinal number of an isolated tree; Indicates the first The multidimensional data points at the th The deepest level in an isolated tree; Represents the ordinal number of the level of an isolated tree; Indicates the first The multidimensional data points at the th The first of the isolated trees The total number of multidimensional data points in the nodes to which the layer belongs; Indicates the first The multidimensional data points at the th The first of the isolated trees The total number of multidimensional data points in the parent node of the node belonging to the layer, which represents the number of data points in the layer. The multidimensional data points at the th The first of the isolated trees The total number of multidimensional data points in the nodes to which the layer belongs; Represented by natural constant An exponential function with base 1.

[0038] in, This represents the ratio of the total number of multidimensional data points in the node to which the data point belongs at any level in the isolated tree to the total number of multidimensional data points in its parent node. The larger the value, the closer the total number of multidimensional data points of the two nodes are, which means that the difference between the data point and other data points during the segmentation process is smaller. In this case, the abnormality of the data point in this dimension is lower.

[0039] At this point, the anomaly level of any multidimensional data point in any dimension has been calculated.

[0040] S3: Calculate the noise level of any multidimensional data point.

[0041] It should be noted that during the data acquisition process, power data may be affected by various noises, such as environmental interference and transient changes in the power system. These noises can impact data quality, therefore, power data needs to be smoothed to improve data quality and reliability.

[0042] It should be further noted that, when smoothing power data, traditional methods use mean filtering with a fixed-size filter window to smooth the power data. However, since different power data have different noise levels, a fixed-size filter window cannot effectively smooth the power data, resulting in an unsatisfactory smoothing effect. Therefore, this invention calculates the noise level of any data point, which facilitates the subsequent determination of the filter window size based on the noise level, thereby making the smoothing result more accurate.

[0043] In one embodiment, the formula for calculating the noise level of any multidimensional data point is as follows: ; In the formula, Indicates the first The noise level of each multidimensional data point; Indicates the total number of dimensions; Indicates the ordinal number of the dimension; Indicates the first The first multidimensional data point The degree of anomaly in each dimension; Indicates the first The mean anomaly degree of all dimensions of a multidimensional data point; Indicates the absolute value symbol; This represents the standard normalization function.

[0044] in, Indicates the first The first multidimensional data point The difference between the degree of anomaly in a dimension and the mean degree of anomaly in all dimensions of the multidimensional data point is considered. The larger the value, the greater the difference between that dimension and other dimensions. In this case, the data in that dimension may be abnormal due to the influence of external factors. Therefore, the greater the difference, the higher the noise level of the multidimensional data point.

[0045] At this point, the noise level calculation for any multidimensional data point is complete.

[0046] S4: Calculate the filtering window based on the noise level of all multidimensional data points within a local range of the multidimensional data points.

[0047] It should be noted that when smoothing any multidimensional data point, noisy data points also need to be smoothed. This invention calculates filter windows of different sizes based on the noise levels of different multidimensional data points to make the smoothing results more accurate. However, if there are noisy data points around the multidimensional data point, the smoothing effect on that multidimensional data point will be affected. Therefore, it is necessary to consider the noise levels of all multidimensional data points within the local area of ​​the multidimensional data point.

[0048] In one embodiment, for any multidimensional data point, the data point closest to the time of its acquisition will be selected. Each multidimensional data point represents a local range of that data point. To preset the number of multidimensional data points within a local range, this invention does not impose a specific limit on it; implementers can adjust it according to the actual situation, for example... .

[0049] The formula for calculating the filtering window of a multidimensional data point based on the noise level of all multidimensional data points within a local range of that multidimensional data point is as follows: ; In the formula, Indicates the first The size of the filtering window for each multidimensional data point; This indicates the size of the preset initial filtering window; Indicates the first The mean noise level of a multidimensional data point and all multidimensional data points within its local range; This represents the function for rounding up.

[0050] It should be noted that, The initial filtering window size is preset, but this invention does not specifically limit it; implementers can adjust it according to actual conditions, for example... .

[0051] Specifically, if the average noise level of the multidimensional data point and all multidimensional data points within its local range is larger, then a larger filtering window is used to filter the data point in order to reduce the impact of noise on the multidimensional data point. If the average noise level of the multidimensional data point and all multidimensional data points within its local range is smaller, then a smaller filtering window is used to filter the data point in order to better preserve the original characteristics of the data point.

[0052] At this point, the calculation of the filter window size for any multidimensional data point is complete.

[0053] S5: Perform filtering based on the size of the filtering window for multi-dimensional data points to complete power data acquisition.

[0054] In one embodiment, for any multidimensional data point, the initial power data is smoothed according to a filtering window to obtain optimal power data. This optimal power data is then uploaded to a concentrator via dual channels of HPLC and HRF. The concentrator deduplicates the optimal power data transmitted through both channels and uploads it to the power consumption information acquisition main station, thus completing the power data acquisition. Uploading to the concentrator via dual channels of HPLC and HRF, as well as deduplicated optimal power data via the concentrator before uploading to the power consumption information acquisition main station, are both existing technologies and will not be elaborated further.

[0055] Through the above steps, the power data smoothing process is completed, making the acquired power data more accurate, and further improving the accuracy of the power data collection results.

[0056] This invention also discloses an intelligent data acquisition system for dual-mode HPLC and HRF communication, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an intelligent data acquisition method for dual-mode HPLC and HRF communication according to the present invention.

[0057] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0058] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (DRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (DRAM), high-bandwidth memory, hybrid memory cube, etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device.

[0059] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

[0060] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A HPLC and HRF dual-mode communication data intelligent acquisition method, characterized in that, The method comprises the steps of: collecting multi-dimensional data points containing voltage, current and power from a power network as initial power data by using HPLC and HRF dual-mode communication architecture, setting an adaptive filter window according to the initial power data, smoothing the initial power data according to the filter window to obtain optimal power data, uploading the optimal power data to a concentrator through HPLC and HRF dual channels, performing deduplication processing on the optimal power data transmitted through the dual channels by the concentrator, and uploading the optimal power data to a power utilization information collection master station, thereby completing power data collection. The setting method of the filter window is as follows: constructing an isolated tree according to the initial power data; for any one dimension of each multi-dimensional data point, calculating a ratio of a total number of multi-dimensional data points in a node to which the multi-dimensional data point belongs in each layer of any isolated tree to a total number of multi-dimensional data points in a parent node of the node; calculating an abnormality degree of the dimension according to the ratio; for each multi-dimensional data point, calculating a difference between the abnormality degree of any one dimension of the multi-dimensional data point and a mean value of abnormality degrees of all dimensions, and calculating a noise degree of the multi-dimensional data point according to an absolute value of the difference and a standard deviation of the abnormality degrees of all dimensions of the multi-dimensional data point; calculating a filter window size of the multi-dimensional data point according to noise degrees of all multi-dimensional data points in a local range of the multi-dimensional data point.

2. The HPLC and HRF dual-mode communication data intelligent acquisition method according to claim 1, characterized in that, The constructing of the isolated tree according to the initial power data comprises the steps of: constructing the isolated tree by taking all the multi-dimensional data points as segmentation samples of an isolated forest algorithm, wherein the segmentation dimension of each layer in the same isolated tree is the same, that is, the same isolated tree is constructed by using data of the same dimension.

3. The HPLC and HRF dual-mode communication data intelligent acquisition method according to claim 1, characterized in that, The abnormality degree satisfies an expression: ； represents the abnormal degree of the th dimension of the th multi-dimensional data point; represents the total number of isolated trees constructed by the th dimension; represents the isolated tree ordinal number; represents the deepest layer number of the th multi-dimensional data point in the th isolated tree; represents the isolated tree layer number ordinal number; represents the total number of multi-dimensional data points in the node to which the th multi-dimensional data point belongs in the th layer of the th isolated tree; represents the total number of multi-dimensional data points in the parent node of the node to which the th multi-dimensional data point belongs in the th layer of the th isolated tree, i.e., represents the total number of multi-dimensional data points in the node to which the th multi-dimensional data point belongs in the th layer of the th isolated tree; represents an exponential function with a natural constant as the base number.

4. The HPLC and HRF dual-mode communication data intelligent acquisition method according to claim 3, characterized in that, The target node of the multi-dimensional data point is obtained in the following manner: for any multi-dimensional data point, if all multi-dimensional data points in any node of the isolated tree contain the multi-dimensional data point, the node is recorded as a target node of the multi-dimensional data point, and all target nodes in each layer of any isolated tree are screened out and used as the nodes to which the multi-dimensional data point belongs in the layer of the isolated tree.

5. The HPLC and HRF dual-mode communication data intelligent acquisition method according to claim 1, characterized in that, The noise degree satisfies an expression: ； wherein represents the noise level of the th multi-dimensional data point; represents the total number of dimensions; represents the dimension ordinal number; represents the anomaly level of the th dimension of the th multi-dimensional data point; represents the anomaly level of all dimensions of the th multi-dimensional data point; represents the absolute value sign; represents the standard normalization function.

6. The HPLC and HRF dual-mode communication data intelligent acquisition method according to claim 1, characterized in that, The local range of the multi-dimensional data point comprises: For any one multi-dimensional data point, the multi-dimensional data point closest to the time instant when the multi-dimensional data point is collected is taken as the local range of the multi-dimensional data point. the multi-dimensional data point closest to the time instant when the multi-dimensional data point is collected is taken as the local range of the multi-dimensional data point.

7. The HPLC and HRF dual-mode communication data intelligent acquisition method according to claim 1, characterized in that, The filter window size satisfies an expression: ； In the formula, Indicates the first The size of the filtering window for each multidimensional data point; This indicates the size of the preset initial filtering window; Indicates the first The mean noise level of a multidimensional data point and all multidimensional data points within its local range; This represents the function for rounding up.

8. A HPLC and HRF dual-mode communication data intelligent acquisition system, characterized in that, comprises: a processor and a memory, the memory storing computer program instructions, when the computer program instructions are executed by the processor, realizing the HPLC and HRF dual-mode communication data intelligent collection method according to any one of claims 1-7.

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