Microgrid equipment test data acquisition method and system based on Beidou positioning

Abstract

The invention discloses a micro-grid equipment test data acquisition method and system based on Beidou positioning, belongs to the field of equipment test data, and carries out information danger degree analysis on normal equipment based on a data transmission contact condition between abnormal equipment and the normal equipment. Position danger degree analysis is performed based on the change condition of the positioning position distance between the abnormal equipment and the normal equipment, equipment transmission danger assessment is performed based on an information danger degree analysis result and a position danger degree analysis result, and data acquisition and transmission of the equipment are performed based on equipment transmission danger assessment result sorting. Under the condition that the data transmission rate is limited, data of key equipment can be transmitted preferentially by analyzing the data transmission contact condition of abnormal equipment and normal equipment, data loss or delay is avoided, and a descending transmission table of data transmission equipment ensures that the data transmission efficiency is improved when resources are limited. And data acquisition and transmission of equipment with relatively high transmission risk are guaranteed preferentially.

Description

Technical Field

[0001] The present invention belongs to the field of equipment test data, and in particular relates to a method and system for collecting test data of microgrid equipment based on Beidou positioning. Background Art

[0002] With the growth of energy demand and the development of smart grid technology, microgrids are gaining increasing attention as an efficient and reliable energy supply method. Microgrids are usually composed of distributed energy resources, loads and energy storage systems. Their stable operation is crucial to the reliability of the power system. In this context, the testing and data collection of microgrid equipment have become important links to ensure system security and optimize operation. In existing technologies, data collection of microgrid equipment mainly relies on Internet of Things technology and traditional data collection methods. These methods can usually collect equipment operating data, such as basic parameters such as voltage, current and temperature. However, these technologies have the following major problems: 1. Insufficient equipment positioning accuracy: Traditional data collection methods lack accurate equipment positioning functions, making it difficult to obtain the geographic location information of equipment in real time. This limits the in-depth analysis of data transmission and position relationships between devices, affecting the rapid positioning of equipment anomalies and the accuracy of system risk assessment; 2. Low data transmission efficiency. When the data transmission rate is limited, existing methods lack targeted data transmission optimization strategies and it is difficult to ensure priority transmission of key equipment data when bandwidth is limited, which may lead to delays or loss of important data, affecting the timeliness and reliability of the system; 3. Lack of assessment of the mutual impact between devices: Existing technologies fail to deeply analyze the data transmission connection between abnormal and normal devices and the impact of changes in their positioning distance on system security. This negligence may lead to the neglect of potential risks and reduce the overall security and stability of the microgrid; In order to solve the above problems, the present invention proposes a microgrid equipment test data acquisition method based on Beidou positioning. By integrating equipment operation data, positioning information and data transmission status, accurate assessment of the degree of equipment abnormality and optimized management of data transmission are achieved. The present invention not only improves the security of the microgrid system, but also improves the efficiency of data acquisition and transmission, providing strong technical support for the development of smart grids. Summary of the Invention

[0003] In response to the deficiencies in the prior art, the present invention proposes a microgrid equipment test data acquisition system and method based on Beidou positioning. The present invention performs an operation data anomaly assessment based on the operation data of each microgrid device during the test process to determine the abnormal device, performs an information risk analysis on the normal device based on the data transmission connection between the abnormal device and the normal device, performs a location risk analysis based on the change in the positioning position distance between the abnormal device and the normal device, performs an equipment transmission risk assessment based on the information risk analysis results and the location risk analysis results, and performs equipment data acquisition and transmission based on the sorting of the equipment transmission risk assessment results. In the case of limited data transmission rate, by analyzing the data transmission connection between the abnormal device and the normal device, the data of the key device can be transmitted first to avoid data loss or delay. The descending transmission table of the data transmission device ensures that when resources are limited, the data acquisition and transmission of the device with higher transmission risk is prioritized, thereby improving the efficiency and reliability of data acquisition.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for collecting test data of microgrid equipment based on Beidou positioning, which includes the following specific steps: S1. Obtain the operating data of each device in the microgrid during the test, the location of the device, and the data transmission between devices; S2. Perform an abnormality evaluation of the operating data based on the operating data of each device in the microgrid during the test to identify abnormal devices; S3. Analyze the information risk level of normal devices based on the data transmission connection between abnormal devices and normal devices; S4. Analyze the degree of location danger based on the change in the location distance between abnormal equipment and normal equipment; S5. Perform equipment transmission risk assessment based on the information risk level analysis results and the location risk level analysis results, and perform equipment data collection and transmission based on the ranking of the equipment transmission risk assessment results.

[0005] It should be noted that as a preferred technical solution for the microgrid equipment test data collection method based on Beidou positioning, the acquisition of operating data of each microgrid device during the test process, the location of the device, and the data transmission between devices includes the following specific steps: S11. Collecting operating data of each device during the test process through a data collection terminal. The operating data of the device is collected through the collection terminal. The collection terminal has an integrated design and multiple data collection functions. The collected data is stored in an operating data storage module. S12. Collecting the location data of the corresponding device in real time through the Beidou satellite positioning module provided on the device and storing it in the location data storage module; S13. Acquire data transmission status between microgrid devices, wherein the data transmission status is represented by the amount of transmitted data, and store the acquired data transmission status in a corresponding storage module.

[0006] It should be noted that as a preferred technical solution for the microgrid equipment test data collection method based on Beidou positioning, the operation data anomaly assessment includes the following specific steps: S21. Obtain the operating data of each device during the microgrid test, and analyze the degree of device abnormality based on the device operating data. The device abnormality analysis formula is: , where T is the monitoring time of the equipment, n is the number of equipment operation data types, ai is the impact weight of the i-th equipment operation data, xit is the specific value of the i-th equipment operation data at the monitoring time t, xim is the median value of the required operation data of the i-th equipment, and the impact weight of the i-th equipment operation data is the impact of the abnormal operation data of the i-th equipment on the equipment, and the value is determined as follows: the proportion of damage caused by data abnormality to the damage caused by the overall abnormality; S22. Obtain the abnormality degree of various devices in the power grid, and compare the obtained abnormality degree of various devices with the set abnormality degree threshold. If the abnormality degree of the device is greater than or equal to the set abnormality degree threshold, the corresponding device is an abnormal device; if the abnormality degree of the device is less than the set abnormality degree threshold, the corresponding device is a normal device.

[0007] It should be noted that as a preferred technical solution for the Beidou positioning-based microgrid equipment test data collection method, the information risk level analysis of normal devices based on the data transmission connection between abnormal devices and normal devices includes the following specific steps: S31. Obtaining the real-time data transmission connection between the normal device and the corresponding abnormal device during the monitoring period, as well as the abnormality degree of the normal device and the abnormality degree of the abnormal device transmitted by the normal device; S32. Import the acquired real-time data transmission connection information between the normal device and the corresponding abnormal device during the monitoring period, the device abnormality level of the normal device, and the device abnormality level of the abnormal device transmitted by the normal device into the information risk level calculation formula of the corresponding normal device to calculate the information risk level of the corresponding normal device. The information risk level calculation formula of the normal device is: , where Ycz is the device abnormality level of the corresponding normal device, m is the number of abnormal devices that transmit information with the corresponding normal device during the monitoring period, Qc is the data transmission volume with the corresponding normal device during the c-th abnormal device monitoring period, Qf is the total data transmission volume of the corresponding normal device during the monitoring period, and Ycc is the device abnormality level during the c-th abnormal device monitoring period; In the formula of this step, Ycz represents the abnormality level of the device itself, which directly corresponds to the operating status of the device (such as CPU usage, memory usage, etc.), where Qc / Qf reflects the impact of the abnormal device on the data transmission of normal devices, Ycc quantifies the risk characteristics of the abnormal device, and (Qc / Qf*Ycc) represents the risk impact of the abnormal device on normal devices through data transmission, which is consistent with the transmission risk between devices in the actual network. m is a normalization parameter used to eliminate the deviation of the number of abnormal devices from the risk calculation.

[0008] It should be noted that as a preferred technical solution for the Beidou positioning-based microgrid equipment test data collection method, the location risk level analysis based on the change in the positioning distance between abnormal devices and normal devices includes the following specific contents: S41, obtaining distance data of abnormal devices near normal devices and device abnormality degree data of the abnormal devices; S42: The acquired distance data of the abnormal device near the corresponding normal device and the data of the corresponding abnormal device are introduced into a position danger degree calculation formula to calculate the position danger degree of the corresponding normal device. The calculation formula of the position danger degree of the corresponding normal device is: , where Qt is the number of abnormal devices near the corresponding normal device at time t, Lm is the standard distance value, that is, the danger caused by the abnormal device will not cause the abnormality of the corresponding normal device beyond this distance, Ycq is the device abnormality degree of the qth abnormal device near the corresponding normal device, Lqt is the distance from the qth abnormal device to the corresponding normal device near time t, and dt is the time integral constant.

[0009] It should be noted that as a preferred technical solution for the Beidou positioning-based microgrid equipment test data collection method, the device transmission risk assessment based on the information risk level analysis results and the location risk level analysis results includes the following specific steps: Obtain the calculated information hazard level analysis results and location hazard level analysis results for the corresponding normal equipment, perform weighted summation to obtain the corresponding normal equipment's equipment transmission hazard level. Since the corresponding normal equipment's information hazard level analysis results and location hazard level analysis results are both unitless values ​​and both represent the degree of hazard, the two are added together to unify the units. At the same time, since the abnormal equipment's location and information transmission jointly affect the normal equipment, the weighted summation method is used to obtain the corresponding normal equipment's equipment transmission hazard level. Obtain the device abnormality degree of the abnormal device, and at the same time obtain the device transmission risk of the normal device, arrange the device abnormality degree of the abnormal device in descending order, and then arrange the device transmission risk of the normal device in descending order after the device abnormality degree of the abnormal device to obtain a data transmission device descending transmission table. When the data transmission rate meets the data acquisition rate, the device data is collected and transmitted in the order of the data transmission device descending transmission table. When the data transmission rate does not meet the data acquisition rate, the data of several devices ranked in front are collected and transmitted in the order of the data transmission device descending transmission table. The several device data here are the data transmission of these devices and the maximum value less than the data transmission rate, that is, if the data transmission rate is 4M / s, and the data that needs to be transmitted and collected for all devices is 4.63M / s, so the data transmission rate does not meet the data acquisition rate, so the devices ranked in front should be selected for data transmission. The data of the devices behind are delayed due to their high security, which ensures that the data of the high-risk devices are collected and transmitted first, thereby ensuring the safety of the microgrid.

[0010] The microgrid equipment test data acquisition system based on Beidou positioning is implemented based on the above-mentioned microgrid equipment test data acquisition method based on Beidou positioning, and specifically includes the following modules: Acquisition module: acquires the operating data of each device in the microgrid during the test, the location of the device, and the data transmission between devices; Abnormal device identification module: Based on the operating data of each device in the microgrid during the test, the abnormal operation data is evaluated to identify abnormal devices; Information risk analysis module: analyzes the information risk level of normal devices based on the data transmission connection between abnormal devices and normal devices; Location danger level analysis module: performs location danger level analysis based on the change in the positioning distance between abnormal equipment and normal equipment; The data acquisition and transmission module performs equipment transmission risk assessment based on the information risk level analysis results and the location risk level analysis results, and performs equipment data acquisition and transmission based on the ranking of equipment transmission risk assessment results.

[0011] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned microgrid equipment test data collection method based on Beidou positioning by calling the computer program stored in the memory.

[0012] A computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the above-mentioned microgrid equipment test data collection method based on Beidou positioning.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention performs an abnormality assessment of operating data based on the operating data during the test of each device in the microgrid to determine abnormal devices, performs information risk analysis on normal devices based on the data transmission connection between abnormal devices and normal devices, performs location risk analysis based on the change in the positioning position distance between abnormal devices and normal devices, performs equipment transmission risk assessment based on the information risk analysis results and the location risk analysis results, and performs data collection and transmission of devices based on the sorting of the equipment transmission risk assessment results. In the case of limited data transmission rate, by analyzing the data transmission connection between abnormal devices and normal devices, data of key devices can be transmitted first to avoid data loss or delay. The descending transmission table of data transmission devices ensures that when resources are limited, data collection and transmission of devices with higher transmission risks are prioritized, thereby improving the efficiency and reliability of data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of the overall process of an embodiment of the method of the present invention; Figure 2 This is a schematic flow chart of step S1 of an embodiment of the method of the present invention; Figure 3 Schematic diagram of the overall framework of the system embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0017] Example 1

[0018] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1-Figure 2 As shown in FIG, a microgrid equipment test data collection method based on Beidou positioning includes the following specific steps: S1. Obtain the operating data of each device in the microgrid during the test, the location of the device, and the data transmission between devices; In this embodiment, S1 includes the following specific steps: S11. Collecting operating data of each device during the test process through a data acquisition terminal. The operating data of the device includes data reflecting the operating safety of the device, such as the voltage, current, and temperature of the device. The operating data of the device is collected through the acquisition terminal. The acquisition terminal has an integrated design and can collect multiple data. The collected data is stored in an operating data storage module. S12. Collecting the location data of the corresponding device in real time through the Beidou satellite positioning module provided on the device and storing it in the location data storage module; S13. Acquire data transmission status between microgrid devices, where the data transmission status is represented by the amount of transmitted data. For example, during the operation of the microgrid, the amount of data transmitted between two devices is 3M. ​​The acquired data transmission status is stored in the corresponding storage module. S2. Perform an abnormality evaluation of the operating data based on the operating data of each device in the microgrid during the test to identify abnormal devices; In this embodiment, the operation data anomaly assessment includes the following specific steps: S21. Obtain the operating data of each device during the microgrid test, and analyze the degree of device abnormality based on the device operating data. The device abnormality analysis formula is: , where T is the monitoring time of the equipment, n is the number of equipment operation data types, ai is the impact weight of the i-th equipment operation data, xit is the specific value of the i-th equipment operation data at the monitoring time t, and xim is the median value of the required operation data of the i-th equipment. For example, the operating voltage required by the equipment at time t is 50V, while the actual operating voltage is 60V, which results in a voltage anomaly difference of 10V. The impact weight of the i-th equipment operation data is the impact of the abnormal operation data of the i-th equipment on the equipment. The value is determined as follows: the proportion of damage caused by data anomalies to the overall abnormal damage. For example, the overall maintenance damage caused by abnormalities in the historical operation of the equipment is 52,000 yuan, while the maintenance damage caused by current anomalies is 13,000 yuan. In this way, the impact weight of current is 0.25; S22. Obtain the abnormality degree of various devices in the power grid, and compare the obtained abnormality degree of various devices with a set abnormality degree threshold. If the abnormality degree of the device is greater than or equal to the set abnormality degree threshold, the corresponding device is an abnormal device; if the abnormality degree of the device is less than the set abnormality degree threshold, the corresponding device is a normal device; S3. Analyze the information risk level of normal devices based on the data transmission connection between abnormal devices and normal devices; In this embodiment, the information risk level analysis of normal devices based on the data transmission connection between abnormal devices and normal devices includes the following specific steps: S31. Obtaining the real-time data transmission connection between the normal device and the corresponding abnormal device during the monitoring period, as well as the abnormality degree of the normal device and the abnormality degree of the abnormal device transmitted by the normal device; S32. Import the acquired real-time data transmission connection information between the normal device and the corresponding abnormal device during the monitoring period, the device abnormality level of the normal device, and the device abnormality level of the abnormal device transmitted by the normal device into the information risk level calculation formula of the corresponding normal device to calculate the information risk level of the corresponding normal device. The information risk level calculation formula of the normal device is: , where Ycz is the device abnormality degree of the corresponding normal device, m is the number of abnormal devices that transmit information with the corresponding normal device during the monitoring period, Qc is the data transmission volume with the corresponding normal device during the c-th abnormal device monitoring period, Qf is the total data transmission volume of the corresponding normal device during the monitoring period, and Ycc is the device abnormality degree during the c-th abnormal device monitoring period; In the formula of this step, Ycz represents the abnormality degree of the device itself, which directly corresponds to the operating status of the device (such as CPU usage, memory usage, etc.), where Qc / Qf reflects the impact of the abnormal device on the data transmission of normal devices, Ycc quantifies the risk characteristics of the abnormal device, and (Qc / Qf*Ycc) represents the risk impact of the abnormal device on normal devices through data transmission, which is consistent with the transmission risk between devices in the actual network. m is a normalization parameter used to eliminate the deviation of the number of abnormal devices from the risk calculation; S4. Analyze the degree of location danger based on the change in the location distance between abnormal equipment and normal equipment; In this embodiment, the location risk level analysis based on the change in the location distance between the abnormal device and the normal device includes the following specific contents: S41, obtaining distance data of abnormal devices near normal devices and device abnormality degree data of the abnormal devices; S42: The acquired distance data of the abnormal device near the corresponding normal device and the data of the corresponding abnormal device are introduced into a position danger degree calculation formula to calculate the position danger degree of the corresponding normal device. The calculation formula of the position danger degree of the corresponding normal device is: , where Qt is the number of abnormal devices near the corresponding normal device at time t, Lm is the standard distance value, that is, the danger caused by the abnormal device will not cause the abnormality of the corresponding normal device beyond this distance, Ycq is the device abnormality level of the qth abnormal device near the corresponding normal device, Lqt is the distance from the qth abnormal device to the corresponding normal device near time t, dt is the time integration constant, each abnormal device can be regarded as a dangerous field source, similar to the distribution of charge or mass in space, the dangerous field intensity of the abnormal device is proportional to its abnormality level (similar to the charge or mass), and as the distance increases, the field intensity decays inversely proportional to the square of the distance, the positional dangerous degree of the normal device is the vector sum of the dangerous field intensities of all abnormal devices, similar to the superposition effect of the field intensities generated by multiple field sources in space; S5. Perform a device transmission risk assessment based on the information risk level analysis results and the location risk level analysis results, and perform device data collection and transmission based on the device transmission risk assessment results; In this embodiment, the device transmission risk assessment based on the information risk level analysis results and the location risk level analysis results includes the following specific steps: Obtain the calculated information hazard level analysis results and location hazard level analysis results of the corresponding normal equipment, perform weighted summation to obtain the device transmission hazard of the corresponding normal equipment. Since the information hazard level analysis results and location hazard level analysis results of the corresponding normal equipment are both unitless values ​​and both represent the degree of hazard, the two are added together to unify the units. At the same time, since the location and information transmission of the abnormal equipment jointly affect the normal equipment, the device transmission hazard of the corresponding normal equipment is obtained by weighted summation. The weight distribution is obtained based on the damage to the equipment affected by nearby equipment and the damage to the transmission equipment. For example, the ratio of the damage to the equipment affected by nearby equipment to the damage affected by data transmission equipment is 8:2, so the weight of the information hazard level analysis result is 0.2, and the weight of the location hazard level analysis result is 0.8; Obtain the device abnormality degree of the abnormal device and the device transmission risk of the normal device at the same time, arrange the device abnormality degree of the abnormal device in descending order, and then arrange the device transmission risk of the normal device in descending order after the device abnormality degree of the abnormal device (as shown in the attached table, the first column of the table is the number of the abnormal device and the normal device, the second column is the device abnormality degree of the abnormal device and the device transmission risk of the normal device, and the third column is the transmission data) to obtain a data transmission device descending transmission table. When the data transmission rate meets the data acquisition rate, the device data is collected and transmitted in the order of the data transmission device descending transmission table. When the data transmission rate does not meet the data acquisition rate, the data is collected and transmitted in the order of the data transmission device descending transmission table. The transmission equipment collects and transmits data from the devices in the front in descending order. The data of the devices here refers to the data transmission sum of these devices, which is less than the maximum value of the data transmission rate. As shown in the attached table, if the data transmission rate is 4M / s, and the data that needs to be transmitted and collected by all devices is 4.63M / s, the data transmission rate does not meet the data collection rate. Therefore, the devices in the front are selected for data transmission (that is, abnormal devices 1, 2, 3, 4, 5 and normal devices 1, 2, 3, 4). The data of the devices in the back are delayed due to their high security. This ensures that the data of the devices with high risk are collected and transmitted first, thus ensuring the safety of the microgrid. Appendix: Equipment abnormality level Transferring Data Abnormal device 1 0.85 0.78M Abnormal device 2 0.76 0.27M Abnormal device 3 0.69 0.32M Abnormal Device 4 0.64 0.78M Abnormal device 5 0.62 0.58M Equipment transmission hazards Normal device 1 1.2 0.24M Normal device 2 1.1 0.67M Normal device 3 0.9 0.14M Normal device 4 0.7 0.21M Normal device 5 0.5 0.32M Normal device 6 0.1 0.32M ; The advantages of this embodiment over the prior art are as follows: an operation data anomaly assessment is performed based on the operation data of each device in the microgrid during the test to determine the abnormal devices, an information risk level analysis is performed on the normal devices based on the data transmission connection between the abnormal devices and the normal devices, a location risk level analysis is performed based on the change in the positioning position distance between the abnormal devices and the normal devices, an equipment transmission risk assessment is performed based on the information risk level analysis results and the location risk level analysis results, and data collection and transmission of the devices are performed based on the sorting of the equipment transmission risk assessment results. In the case of limited data transmission rate, by analyzing the data transmission connection between the abnormal devices and the normal devices, the data of the key devices can be transmitted first to avoid data loss or delay. The descending order transmission table of the data transmission devices ensures that when resources are limited, data collection and transmission of devices with higher transmission risks are prioritized, thereby improving the efficiency and reliability of data collection.

[0019] Example 2

[0020] like Figure 3 As shown, a microgrid equipment test data acquisition system based on Beidou positioning is implemented based on the above-mentioned microgrid equipment test data acquisition method based on Beidou positioning, and specifically includes: an acquisition module: acquiring the operating data of each microgrid device during the test process, the positioning position of the device, and the data transmission status between the devices; an abnormal device determination module: performing an operation data abnormality assessment based on the operating data of each microgrid device during the test process to determine the abnormal device; an information risk level analysis module: performing an information risk level analysis on the normal device based on the data transmission connection between the abnormal device and the normal device; a position risk level analysis module: performing a position risk level analysis based on the change in the positioning position distance between the abnormal device and the normal device; a data acquisition and transmission module, performing an equipment transmission risk assessment based on the information risk level analysis results and the position risk level analysis results, and performing equipment data acquisition and transmission based on the sorting of the equipment transmission risk assessment results. The specific steps of the above modules in this embodiment are all specifically described in the above-mentioned method embodiment, and will not be repeated in this embodiment.

[0021] Example 3

[0022] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned microgrid equipment test data collection method based on Beidou positioning by calling the computer program stored in the memory.

[0023] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the microgrid equipment test data collection method based on Beidou positioning provided by the above method embodiment, which includes the following specific steps: S1. Obtain the operating data of each device in the microgrid during the test, the location of the device, and the data transmission between devices; S2. Perform an abnormality evaluation of the operating data based on the operating data of each device in the microgrid during the test to identify abnormal devices; S3. Analyze the information risk level of normal devices based on the data transmission connection between abnormal devices and normal devices; S4. Analyze the degree of location danger based on the change in the location distance between abnormal equipment and normal equipment; S5. Based on the information risk level analysis results and the location risk level analysis results, a device transmission risk assessment is performed. Data collection and transmission of the device is performed based on the ranking of the device transmission risk assessment results. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface to facilitate data input and output. This embodiment is not further described here.

[0024] Example 4

[0025] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program runs on a computer device, the computer device executes the above-mentioned microgrid equipment test data collection method based on Beidou positioning.

[0026] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0027] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0028] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A microgrid equipment test data acquisition method based on Beidou positioning, characterized in that: It includes the following specific steps: S1. Obtain the operating data of each device in the microgrid during the test, the location of the device, and the data transmission between devices; S2. Perform an abnormality evaluation of the operating data based on the operating data of each device in the microgrid during the test to identify abnormal devices; S3. Analyze the information risk level of normal devices based on the data transmission connection between abnormal devices and normal devices; S4. Analyze the degree of location danger based on the change in the location distance between abnormal equipment and normal equipment; S5. Perform equipment transmission risk assessment based on the information risk level analysis results and the location risk level analysis results, and perform equipment data collection and transmission based on the ranking of the equipment transmission risk assessment results.

2. The microgrid equipment test data acquisition method based on Beidou positioning according to claim 1, characterized in that: The operation data anomaly assessment includes the following specific steps: S21. Obtaining the operating data of each device during the microgrid test, and analyzing the degree of device abnormality based on the device operating data; S22. Obtain the abnormality degree of various devices in the power grid, and compare the obtained abnormality degree of various devices with the set abnormality degree threshold. If the abnormality degree of the device is greater than or equal to the set abnormality degree threshold, the corresponding device is an abnormal device; if the abnormality degree of the device is less than the set abnormality degree threshold, the corresponding device is a normal device.

3. The microgrid equipment test data acquisition method based on Beidou positioning according to claim 2 is characterized in that: The information risk analysis of normal devices based on the data transmission connection between abnormal devices and normal devices includes the following specific steps: S31. Obtaining the real-time data transmission connection between the normal device and the corresponding abnormal device during the monitoring period, as well as the abnormality degree of the normal device and the abnormality degree of the abnormal device transmitted by the normal device; S32. Import the acquired real-time data transmission connection information between the normal device and the corresponding abnormal device during the monitoring period, the device abnormality level of the normal device, and the device abnormality level of the abnormal device transmitted by the normal device into the information risk level calculation formula of the corresponding normal device to calculate the information risk level of the corresponding normal device. The information risk level calculation formula of the normal device is: , where Ycz is the device abnormality degree of the corresponding normal device, m is the number of abnormal devices that transmit information with the corresponding normal device during the monitoring period, Qc is the data transmission volume with the corresponding normal device during the c-th abnormal device monitoring period, Qf is the total data transmission volume corresponding to the normal device during the monitoring period, and Ycc is the device abnormality degree of the c-th abnormal device monitoring period.

4. The microgrid equipment test data acquisition method based on Beidou positioning according to claim 3 is characterized in that: The location risk level analysis based on the change in the positioning distance between abnormal equipment and normal equipment includes the following specific contents: S41, obtaining distance data of abnormal devices near normal devices and device abnormality degree data of the abnormal devices; S42: The acquired distance data of the abnormal device near the corresponding normal device and the data of the corresponding abnormal device are introduced into a position danger degree calculation formula to calculate the position danger degree of the corresponding normal device. The calculation formula of the position danger degree of the corresponding normal device is: , where Qt is the number of abnormal devices near the corresponding normal device at time t, Lm is the distance standard value, Ycq is the device abnormality degree of the qth abnormal device near the corresponding normal device, Lqt is the distance from the qth abnormal device to the corresponding normal device near time t, and dt is the time integration constant.

5. The microgrid equipment test data acquisition method based on Beidou positioning according to claim 4 is characterized in that: The device transmission risk assessment based on the information risk level analysis results and the location risk level analysis results includes the following specific steps: Obtain the calculated information risk level analysis results and location risk level analysis results of the corresponding normal equipment, and perform weighted summation to obtain the device transmission risk level of the corresponding normal equipment; Obtain the device abnormality degree of the abnormal device, and simultaneously obtain the device transmission risk of the normal device, arrange the device abnormality degrees of the abnormal device in descending order, and then arrange the device transmission risk of the normal device in descending order after the device abnormality degrees of the abnormal device to obtain a descending transmission table of data transmission devices. When the data transmission rate meets the data acquisition rate, collect and transmit device data in the order of the data transmission device descending transmission table. When the data transmission rate does not meet the data acquisition rate, collect and transmit data of several devices sorted in front in the order of the data transmission device descending transmission table.

6. The method for collecting test data of microgrid equipment based on Beidou positioning according to claim 2, wherein: The equipment abnormality degree analysis formula is: , where T is the monitoring time of the equipment, n is the number of types of equipment operation data, ai is the influence weight of the i-th equipment operation data, xit is the specific value of the i-th equipment operation data at the monitoring time t, and xim is the median value of the required operation data of the i-th equipment.

7. The microgrid equipment test data acquisition method based on Beidou positioning according to claim 6, characterized in that: The acquisition of the operating data of each device in the microgrid during the test, the location of the device, and the data transmission between the devices includes the following specific steps: S11. Collecting operating data of each device during the test process through a data collection terminal. The operating data of the device is collected through the collection terminal. The collection terminal has an integrated design and multiple data collection functions. The collected data is stored in an operating data storage module. S12. Collecting the location data of the corresponding device in real time through the Beidou satellite positioning module provided on the device and storing it in the location data storage module; S13. Acquire data transmission status between microgrid devices, wherein the data transmission status is represented by the amount of transmitted data, and store the acquired data transmission status in a corresponding storage module.

8. A microgrid equipment test data acquisition system based on Beidou positioning, which is implemented based on the microgrid equipment test data acquisition method based on Beidou positioning according to any one of claims 1 to 7, characterized in that: It specifically includes the following modules: Acquisition module: acquires the operating data of each device in the microgrid during the test, the location of the device, and the data transmission between devices; Abnormal device identification module: Based on the operating data of each device in the microgrid during the test, the abnormal operation data is evaluated to identify abnormal devices; Information risk analysis module: analyzes the information risk level of normal devices based on the data transmission connection between abnormal devices and normal devices; Location danger level analysis module: performs location danger level analysis based on the change in the positioning distance between abnormal equipment and normal equipment; The data acquisition and transmission module performs equipment transmission risk assessment based on the information risk level analysis results and the location risk level analysis results, and performs equipment data acquisition and transmission based on the ranking of equipment transmission risk assessment results.

9. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the microgrid equipment test data acquisition method based on Beidou positioning as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the microgrid equipment test data collection method based on Beidou positioning as described in any one of claims 1 to 7.

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