Micro-grid equipment test data acquisition method and system based on Beidou positioning

Abstract

The application discloses a micro-grid equipment test data acquisition method and system based on Beidou positioning, and belongs to the field of equipment test data. The application performs information danger degree analysis on normal equipment based on data transmission contact conditions of abnormal equipment and the normal equipment, performs position danger degree analysis based on position distance change conditions of the abnormal equipment and the normal equipment, performs equipment transmission danger assessment based on information danger degree analysis results and position danger degree analysis results, and performs data acquisition and transmission of the equipment based on equipment transmission danger assessment result sorting. In the case that the data transmission rate is limited, the data of key equipment can be preferentially transmitted by analyzing the data transmission contact conditions of the abnormal equipment and the normal equipment, data loss or delay is avoided, and the descending transmission table of the data transmission equipment ensures that the data acquisition and transmission of equipment with high transmission danger are preferentially ensured when resources are limited.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of equipment test data, and in particular to a micro-grid equipment test data acquisition method and system based on Beidou positioning. BACKGROUND

[0002] With the growth of energy demand and the development of smart grid technology, micro-grid as an efficient and reliable energy supply method is increasingly valued. Micro-grid is usually composed of distributed energy resources, loads and energy storage systems, and its stable operation is crucial to the reliability of the power system. In this context, the testing and data acquisition of micro-grid equipment have become an important link to ensure system safety and optimize operation. In the prior art, the data acquisition of micro-grid equipment mainly relies on Internet of Things technology and traditional data acquisition methods. These methods can usually collect equipment operating data such as voltage, current and temperature, etc. However, these technologies have the following main problems:

[0003] 1. Insufficient equipment positioning accuracy: Traditional data acquisition methods lack precise equipment positioning function, making it difficult to obtain real-time geographic location information of equipment, which limits the in-depth analysis of data transmission and location relationship between equipment, affecting the rapid positioning of equipment abnormalities and the accuracy of system risk assessment; 2. Low data transmission efficiency: In the case of limited data transmission rate, existing methods lack targeted data transmission optimization strategies, making it difficult to ensure the priority transmission of critical equipment data when bandwidth is limited, which may cause important data delay or loss, affecting the timeliness and reliability of the system; 3. Lack of evaluation of mutual influence between equipment: The existing technology fails to deeply analyze the data transmission relationship between abnormal equipment and normal equipment and the impact of the change of positioning distance on system safety, which may lead to neglect of potential risks, reducing the overall safety and stability of the micro-grid;

[0004] To solve the above problems, the present application proposes a micro-grid equipment test data acquisition method based on Beidou positioning, which integrates equipment operating data, positioning information and data transmission conditions to realize accurate evaluation of equipment abnormality and optimized management of data transmission. The present application not only improves the safety of micro-grid system, but also improves the efficiency of data acquisition and transmission, providing strong technical support for the development of smart grid. SUMMARY

[0005] In view of the deficiencies of the prior art, the application provides a micro-grid equipment test data acquisition system and method based on Beidou positioning, which performs operation data anomaly evaluation based on operation data in the test process of each equipment of the micro-grid to determine abnormal equipment, performs information risk degree analysis on normal equipment based on data transmission contact conditions of the abnormal equipment and the normal equipment, performs position risk degree analysis based on changes in the positioning position distance of the abnormal equipment and the normal equipment, performs equipment transmission risk evaluation based on the information risk degree analysis result and the position risk degree analysis result, performs data acquisition and transmission of the equipment based on the equipment transmission risk evaluation result ranking, and in the case of limited data transmission rate, the data of key equipment can be preferentially transmitted by analyzing the data transmission contact conditions of the abnormal equipment and the normal equipment, data loss or delay is avoided, and the descending transmission table of the data transmission equipment ensures that the data acquisition and transmission of equipment with high transmission risk are preferentially ensured when resources are limited, and the efficiency and reliability of data acquisition are improved.

[0006] To achieve the above object, the application provides the following technical scheme: a micro-grid equipment test data acquisition method based on Beidou positioning, which comprises the following specific steps:

[0007] S1, obtaining operation data in the test process of each equipment of the micro-grid, positioning positions of the equipment, and data transmission conditions between the equipment;

[0008] S2, performing operation data anomaly evaluation based on the operation data in the test process of each equipment of the micro-grid to determine abnormal equipment;

[0009] S3, performing information risk degree analysis on normal equipment based on data transmission contact conditions of the abnormal equipment and the normal equipment;

[0010] S4, performing position risk degree analysis based on changes in the positioning position distance of the abnormal equipment and the normal equipment;

[0011] S5, performing equipment transmission risk evaluation based on the information risk degree analysis result and the position risk degree analysis result, and performing data acquisition and transmission of the equipment based on the equipment transmission risk evaluation result ranking.

[0012] It should be noted that, as a preferred technical scheme of the micro-grid equipment test data acquisition method based on Beidou positioning, the obtaining of the operation data in the test process of each equipment of the micro-grid, the positioning positions of the equipment, and the data transmission conditions between the equipment comprises the following specific steps:

[0013] S11, collecting operation data of each equipment in the test process by a data acquisition terminal, wherein the operation data of the equipment is collected by the acquisition terminal, the acquisition terminal has multiple data collection functions through integrated design, and the collected data is stored in an operation data storage module;

[0014] S12, collecting position data of the corresponding device in real time through the Beidou satellite positioning module arranged on the device and storing the position data in a position data storage module;

[0015] S13, acquiring data transmission conditions between the micro-grid devices, wherein the data transmission conditions are represented by the amount of transmission data, and the acquired data transmission conditions are stored in the corresponding storage module.

[0016] It should be noted that, as a preferred technical solution of the micro-grid device test data acquisition method based on Beidou positioning, the operation data anomaly evaluation includes the following specific steps:

[0017] S21, acquiring operation data of each device in the micro-grid test process, and analyzing the device abnormality degree based on the device operation data, wherein the device abnormality degree analysis formula is: wherein T is the monitoring time length of the device, n is the number of device operation data types, ai is the influence weight of the ith device operation data, xit is the specific value of the ith device operation data at monitoring time t, xim is the median value of the ith device operation data, and the influence weight of the ith device operation data is the influence size of the ith device operation data anomaly on the device, and the value is taken in the following manner: the damage caused by the data anomaly accounts for the proportion of the overall anomaly damage;

[0018] S22, acquiring the abnormality degree of various devices, comparing the acquired 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, and if the abnormality degree of the device is less than the set abnormality degree threshold, the corresponding device is a normal device.

[0019] It should be noted that, as a preferred technical solution of the micro-grid device test data acquisition method based on Beidou positioning, the information danger degree analysis of the normal device based on the data transmission contact conditions of the abnormal device and the normal device includes the following specific steps:

[0020] S31, acquiring the data transmission contact conditions of the normal device and the corresponding abnormal device in real time in the monitoring period, and the device abnormality degree of the normal device and the device abnormality degree of the abnormal device transmitted by the normal device;

[0021] S32, introducing the acquired data transmission contact conditions of the normal device and the corresponding abnormal device in real time in the monitoring period, and the device abnormality degree of the normal device and the device abnormality degree of the abnormal device transmitted by the normal device into the information danger degree calculation formula of the corresponding normal device to calculate the information danger degree of the corresponding normal device, wherein the information danger degree calculation formula of the normal device is: Wherein, Ycz is the device abnormality degree corresponding to the normal device, m is the number of abnormal devices transmitting information with the corresponding normal device in the monitoring period, Qc is the data transmission amount of the cth abnormal device monitoring period with the corresponding normal device, Qf is the total data transmission amount of the corresponding normal device in the monitoring period, Ycc is the device abnormality degree of the cth abnormal device monitoring period; in the formula of this step, Ycz represents the abnormality degree of the device itself, which directly corresponds to the running state of the device (such as CPU usage, memory usage, etc.), wherein Qc / Qf reflects the influence degree of abnormal device on normal device data transmission, Ycc quantifies the risk characteristics of abnormal device, and (Qc / Qf*Ycc) represents the risk influence of abnormal device on normal device through data transmission, which is consistent with the propagation risk between devices in the actual network, and m is a normalization parameter, which is used to eliminate the deviation of abnormal device quantity on risk calculation.

[0022] It should be noted that, as the preferred technical solution of the Beidou positioning-based micro-grid device test data acquisition method, the position risk degree analysis based on the distance change of the abnormal device and the normal device includes the following specific contents:

[0023] S41, acquiring distance data of abnormal devices near the corresponding normal device and device abnormality degree data of the corresponding abnormal device;

[0024] S42, importing the acquired distance data of abnormal devices near the corresponding normal device and the data of the corresponding abnormal device into the position risk degree calculation formula to calculate the position risk degree of the corresponding normal device, wherein the calculation formula of the position risk degree of the corresponding normal device is: Wherein, Qt is the number of abnormal devices near the corresponding normal device at t time, Lm is the distance standard value, that is, the danger caused by the abnormal device beyond this distance will not cause the abnormality of the corresponding normal device, 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 near the corresponding normal device at t time, and dt is the time integral constant.

[0025] It should be noted that, as the preferred technical solution of the Beidou positioning-based micro-grid device test data acquisition method, the device transmission risk assessment based on the information risk degree analysis result and the position risk degree analysis result includes the following specific steps:

[0026] The information danger degree analysis result and the position danger degree analysis result of the corresponding normal device are obtained, weighted summation is performed, and the device transmission danger of the corresponding normal device is obtained. Since the information danger degree analysis result and the position danger degree analysis result of the corresponding normal device are both unitless values and both represent the degree of danger, the two are added and the units are unified. Since the position and information transmission of the abnormal device jointly affect the normal device, the weighted summation is used to obtain the device transmission danger of the corresponding normal device.

[0027] The device abnormality degree of the abnormal device is obtained, the device transmission danger of the normal device is obtained, the device abnormality degree of the abnormal device is arranged in descending order, then the device transmission danger of the normal device is arranged 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 a certain number of devices arranged in front is collected and transmitted in the order of the data transmission device descending transmission table. The certain number of devices is the maximum value of the data transmission of these devices and is less than the data transmission rate. For example, if the data transmission rate is 4M / s and the data to be collected and transmitted of all devices is 4.63M / s, the data transmission rate does not meet the data acquisition rate, so the devices arranged in front are selected for data transmission. The data of the devices arranged in the rear is delayed due to high safety, which ensures that the data of the devices with high danger is collected and transmitted in priority, and the safety of the micro-grid is ensured.

[0028] The micro-grid device test data acquisition system based on Beidou positioning is implemented based on the above-mentioned micro-grid device test data acquisition method based on Beidou positioning, and specifically includes the following modules:

[0029] The acquisition module acquires the running data of each device in the micro-grid test process, the positioning position of the device, and the data transmission between the devices.

[0030] The abnormal device determination module performs running data abnormality evaluation based on the running data of each device in the micro-grid test process to determine the abnormal device.

[0031] The information danger degree analysis module performs information danger degree analysis on the normal device based on the data transmission contact between the abnormal device and the normal device.

[0032] The position danger degree analysis module performs position danger degree analysis based on the change of the positioning position distance between the abnormal device and the normal device.

[0033] The data acquisition and transmission module performs equipment transmission risk assessment based on the information risk degree analysis result and the position risk degree analysis result, and performs data acquisition and transmission of the equipment based on the equipment transmission risk assessment result ranking.

[0034] An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be invoked by the processor;

[0035] The processor executes the above-mentioned Beidou positioning-based micro-grid equipment test data acquisition method by invoking the computer program stored in the memory.

[0036] A computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the above-mentioned Beidou positioning-based micro-grid equipment test data acquisition method.

[0037] Compared with the prior art, the present application has the following beneficial effects:

[0038] The present application performs operation data anomaly evaluation based on the operation data in the test process of each equipment of the micro-grid to determine abnormal equipment, performs information risk degree analysis on normal equipment based on the data transmission contact situation of the abnormal equipment and the normal equipment, performs position risk degree analysis based on the change of the positioning position distance of the abnormal equipment and the normal equipment, performs equipment transmission risk assessment based on the information risk degree analysis result and the position risk degree analysis result, and performs data acquisition and transmission of the equipment based on the equipment transmission risk assessment result ranking. In the case of limited data transmission rate, by analyzing the data transmission contact situation of the abnormal equipment and the normal equipment, the data of the key equipment can be transmitted preferentially to avoid data loss or delay. The descending transmission table of the data transmission equipment ensures that when the resources are limited, the data acquisition and transmission of the equipment with high transmission risk are preferentially guaranteed, and the efficiency and reliability of data acquisition are improved. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is a whole flowchart of the method embodiment of the present application.

[0040] Figure 2 It is a flowchart of S1 step of the method embodiment of the present application.

[0041] Figure 3 It is a whole framework diagram of the system embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0043] It should also be noted that the relative terms such as first and second in the present specification are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0044] Embodiment 1

[0045] To solve the technical problems proposed in the background art, the present application provides a preferred embodiment: as shown in the figure, the micro-grid equipment test data acquisition method based on Beidou positioning includes the following specific steps: Figures 1-2

[0046] S1, obtaining the running data in the test process of each equipment of the micro-grid, the positioning position of the equipment and the data transmission situation between the equipment;

[0047] In the present embodiment, S1 includes the following specific steps:

[0048] S11, collecting the running data of each equipment in the test process through a data acquisition terminal, wherein the running data of the equipment includes the voltage, current and temperature of the equipment and other data reflecting the safety of the equipment operation, wherein the running data of the equipment is collected through the acquisition terminal, the acquisition terminal has multiple data acquisition functions through integrated design, and the collected data is stored in the running data storage module;

[0049] S12, collecting the position data of the corresponding equipment in real time through a Beidou satellite positioning module arranged on the equipment and storing the position data in the position data storage module;

[0050] ​S13, acquire data transmission between micro-grid devices, wherein the data transmission is represented by the amount of transmission data, for example, the amount of data transmission between two devices is 3M during the operation of the micro-grid, and the acquired data transmission is stored in the corresponding storage module;

[0051] S2, perform operation data anomaly evaluation based on the operation data of each device in the micro-grid test process to determine abnormal devices;

[0052] In this embodiment, the operation data anomaly evaluation includes the following specific steps:

[0053] S21, acquire the operation data of each device in the micro-grid test process, and perform device anomaly degree analysis based on the device operation data, wherein the device anomaly degree analysis formula is: Wherein, T is the monitoring time length of the device, n is the number of device operation data types, ai is the influence weight of the ith device operation data, xit is the specific value of the ith device operation data at monitoring time t, xim is the median value of the ith device operation data, for example, the device needs to operate at t The operating voltage is 50V, and the actual operating voltage is 60V, which produces a voltage abnormality difference of 10V, and the influence weight of the ith device operation data is the influence of the ith device operation data anomaly on the device, The value is: the damage caused by data anomaly accounts for the proportion of the overall damage, for example, the damage caused by the abnormality of the device in the historical operation process is 52,000 yuan, and the damage caused by the abnormality of the current is 13,000 yuan, so the influence weight of the current is 0.25;

[0054] S22, acquire the abnormality degree of various devices, and compare the acquired abnormality degree of various devices with the set abnormality degree threshold value, if the abnormality degree of the device is greater than or equal to the set abnormality degree threshold value, the corresponding device is an abnormal device, if the abnormality degree of the device is less than the set abnormality degree threshold value, the corresponding device is a normal device;

[0055] S3, perform information danger degree analysis on normal devices based on the data transmission contact situation of abnormal devices and normal devices;

[0056] In this embodiment, the information danger degree analysis on normal devices based on the data transmission contact situation of abnormal devices and normal devices includes the following specific steps:

[0057] S31, acquire the data transmission contact situation of normal devices and corresponding abnormal devices in real time in the monitoring period, and the device abnormality degree situation of normal devices and the device abnormality degree situation of abnormal devices transmitted by normal devices;

[0058] S32, the data transmission contact situation of the acquired monitoring period real-time normal equipment and corresponding abnormal equipment, and the device abnormality degree situation of the normal equipment and the device abnormality degree situation of the abnormal equipment transmitted by the normal equipment are introduced into the information danger degree calculation formula of the corresponding normal equipment to calculate the information danger degree of the corresponding normal equipment, wherein the information danger degree calculation formula of the normal equipment is: wherein Ycz is the device abnormality degree of the corresponding normal equipment, m is the number of abnormal equipment for information transmission with the corresponding normal equipment in the monitoring period, Qc is the data transmission amount of the cth abnormal equipment monitoring period with the corresponding normal equipment, Qf is the total data transmission amount of the corresponding normal equipment in the monitoring period, Ycc is the device abnormality degree of the cth abnormal equipment monitoring period; in the formula in this step, Ycz represents the abnormality degree of the device itself, which directly corresponds to the running state of the device (such as CPU usage, memory usage, etc.), wherein Qc / Qf reflects the influence degree of the abnormal equipment on the data transmission of the normal equipment, Ycc quantifies the risk characteristics of the abnormal equipment, and (Qc / Qf*Ycc) represents the risk influence of the abnormal equipment on the normal equipment through data transmission, which is consistent with the propagation risk between devices in the actual network, and m is a normalization parameter for eliminating the deviation of the number of abnormal equipment on risk calculation;

[0059] S4, position danger degree analysis based on the change of the positioning location distance between the abnormal equipment and the normal equipment;

[0060] In this embodiment, the position danger degree analysis based on the change of the positioning location distance between the abnormal equipment and the normal equipment includes the following specific contents:

[0061] S41, acquiring distance data of abnormal equipment near the corresponding normal equipment and device abnormality degree data of the corresponding abnormal equipment;

[0062] S42, introducing the acquired distance data of the abnormal equipment near the corresponding normal equipment and the data of the corresponding abnormal equipment into the position danger degree calculation formula to calculate the position danger degree of the corresponding normal equipment, wherein the calculation formula of the position danger degree of the corresponding normal equipment is: Wherein Qt is the number of abnormal devices near the corresponding normal device at time t, Lm is the distance standard value, that is, the danger caused by abnormal devices beyond this distance will not cause abnormality of the corresponding normal device, 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 near at time t to the corresponding normal device, and dt is the time integration constant. Each abnormal device can be regarded as a danger field source similar to charge or mass distribution in space. The danger field strength of the abnormal device is proportional to its abnormality degree (similar to charge or mass), and the field strength decreases inversely with the square of the distance. The position danger degree of the normal device is the vector sum of the danger field strengths of all abnormal devices, similar to the superposition effect of field strengths generated by multiple field sources in space.

[0063] S5, based on the information danger degree analysis result and the position danger degree analysis result, performing device transmission danger assessment, and based on the device transmission danger assessment result, sorting the data collection and transmission of the device;

[0064] In the embodiment, the device transmission danger assessment based on the information danger degree analysis result and the position danger degree analysis result comprises the following specific steps:

[0065] The calculated information danger degree analysis result and the position danger degree analysis result of the corresponding normal device are obtained, and the device transmission danger of the corresponding normal device is obtained by weighted summation. Since the information danger degree analysis result and the position danger degree analysis result of the corresponding normal device are both unitless values and both represent the degree of danger, the two are added and the units are unified. Since the position and information transmission of the abnormal device jointly affect the normal device, the device transmission danger of the corresponding normal device is obtained by using the weighted summation method. The weight distribution is obtained according to the damage of the device affected by the nearby device and the damage of the device affected by the data transmission device. For example, the proportion of the damage of the device affected by the nearby device to the damage of the device affected by the data transmission device is 8:2. Therefore, the weight of the information danger degree analysis result is 0.2, and the weight of the position danger degree analysis result is 0.8.

[0066] The device abnormality degree of the abnormal device is obtained, the device transmission risk of the normal device is obtained, the device abnormality degree of the abnormal device is arranged in descending order, and then the device transmission risk of the normal device is arranged 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 descending transmission table of the data transmission device, when the data transmission rate meets the data acquisition rate, the device data is collected and transmitted in the order of the descending transmission table of the data transmission device, when the data transmission rate does not meet the data acquisition rate, the device data of the front several devices is collected and transmitted in the order of the descending transmission table of the data transmission device, the data of the several devices is the maximum value that is less than the data transmission rate, that is, as shown in the attached table, if the data transmission rate is 4M / s, and the data to be collected and transmitted of all the devices is 4.63M / s, the data transmission rate does not meet the data acquisition rate, so the devices in the front are selected for data transmission (that is, the abnormal devices 1, 2, 3, 4, 5 and the normal devices 1, 2, 3, 4), the data of the devices behind is delayed due to high safety, so that the data of the devices with high risk is collected and transmitted preferentially, and the safety of the micro-grid is ensured.

[0067] The attached table is as follows:

[0068] Device abnormality degree Transmitted 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 Device transmission danger 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

[0069] The advantages of the embodiment over the prior art are as follows: the running data of each device in the micro-grid is used to evaluate the running data abnormality, to determine the abnormal device, the information risk degree of the normal device is analyzed based on the data transmission relationship between the abnormal device and the normal device, the position risk degree is analyzed based on the distance change of the positioning location of the abnormal device and the normal device, the device transmission risk is evaluated based on the information risk degree analysis result and the position risk degree analysis result, the data of the device is collected and transmitted based on the sorting result of the device transmission risk evaluation, in the case of limited data transmission rate, the data of the key device can be transmitted preferentially by analyzing the data transmission relationship between the abnormal device and the normal device, data loss or delay is avoided, and the descending transmission table of the data transmission device ensures that the data collection and transmission of the device with high transmission risk is preferentially ensured when the resources are limited, and the efficiency and reliability of data collection are improved.

[0070] Embodiment 2

[0071] As Figure 3As shown, the micro-grid equipment test data collection system based on Beidou positioning is realized based on the above-mentioned micro-grid equipment test data collection method based on Beidou positioning, and specifically includes: an acquisition module: acquiring the running data of each device in the micro-grid test process, the positioning position of the device, and the data transmission between the devices; an abnormal device determination module: performing running data anomaly evaluation based on the running data of each device in the micro-grid test process to determine abnormal devices; an information risk degree analysis module: performing information risk degree analysis on normal devices based on the data transmission contact situation of abnormal devices and normal devices; a position risk degree analysis module: performing position risk degree analysis based on the positioning position distance change situation of abnormal devices and normal devices; a data collection and transmission module: performing device transmission risk evaluation based on the information risk degree analysis result and the position risk degree analysis result, and performing data collection and transmission of the device based on the device transmission risk evaluation result sorting. The specific steps of the above modules of the embodiment are specifically described in the method embodiment, and will not be described in detail in this embodiment.

[0072] Embodiment 3

[0073] The embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0074] The processor executes the above-mentioned micro-grid equipment test data collection method based on Beidou positioning by calling the computer program stored in the memory.

[0075] The electronic device can have a large difference due to different configurations or performances, and can include one or more processors and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to realize the micro-grid equipment test data collection method based on Beidou positioning provided by the above-mentioned method embodiment, which includes the following specific steps:

[0076] S1, acquiring the running data of each device in the micro-grid test process, the positioning position of the device, and the data transmission between the devices;

[0077] S2, performing running data anomaly evaluation based on the running data of each device in the micro-grid test process to determine abnormal devices;

[0078] S3, performing information risk degree analysis on normal devices based on the data transmission contact situation of abnormal devices and normal devices;

[0079] S4, performing position risk degree analysis based on the positioning position distance change situation of abnormal devices and normal devices;

[0080] S5, based on the information risk degree analysis result and the position risk degree analysis result, performing device transmission risk assessment, and based on the device transmission risk assessment result, performing sorting of data collection and transmission of the device, for example, the electronic device can further have a wired or wireless network interface and an input and output interface and the like to perform input and output of data. This embodiment will not be described here.

[0081] Embodiment 4

[0082] The embodiment provides a computer readable storage medium, which stores an erasable computer program.

[0083] When the computer program runs on the computer device, the computer device performs the above-mentioned micro-grid device test data collection method based on Beidou positioning.

[0084] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only optical disc, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0085] The above-mentioned embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above-mentioned embodiments can be realized in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the flow or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through a wired network or / and a wireless network. The computer readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center and the like containing one or more available medium sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD) or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0086] The preferred embodiments of the application disclosed above are only to facilitate the elucidation of the application. The preferred embodiments do not describe all the details of the application and limit the application to the specific embodiments described. Obviously, many modifications and variations can be made in light of the teachings above. The description is chosen and described in order to best explain the principles of the application and its practical application to thereby enable others skilled in the art to best utilize the application and get the best results from the application. The application is only limited by the claims and their full scope and equivalents.

Claims

1. A method for acquiring test data of microgrid equipment based on BeiDou positioning, characterized in that, It includes the following specific steps: S1. Acquire the operating data, device location, and data transmission between devices during the testing process of each device in the microgrid; S2. Based on the operational data during the testing process of each device in the microgrid, conduct anomaly assessment of operational data to identify abnormal devices; S3. Analyze the information risk level of normal devices based on the data transmission communication between abnormal devices and normal devices; The specific steps include the following: S31. Obtain the real-time data transmission communication status between normal devices and corresponding abnormal devices during the monitoring period, as well as the degree of abnormality of normal devices and the degree of abnormality of abnormal devices transmitted by normal devices. S32. Import the data transmission communication information between normal devices and corresponding abnormal devices in real time during the monitoring period, as well as the abnormality level of the normal devices and the abnormality level of the abnormal devices transmitted by the normal devices, into the information risk level calculation formula for the corresponding normal devices to calculate the information risk level of the corresponding normal devices. The information risk level calculation formula for normal devices is as follows: Where Ycz represents the degree of device abnormality of the corresponding normal device, m represents the number of abnormal devices that transmit information with the corresponding normal device within the monitoring period, Qc represents the amount of data transmitted between the abnormal device and the corresponding normal device within the c-th monitoring period, Qf represents the total amount of data transmitted between the corresponding normal device within the monitoring period, and Ycc represents the degree of device abnormality of the c-th monitoring period. S4. Analyze the degree of location hazard based on the changes in the distance between the abnormal and normal equipment locations; including the following specific contents: S41. Obtain the distance data of abnormal devices near the corresponding normal devices and the abnormality level data of the corresponding abnormal devices; S42. Import the distance data of the abnormal devices near the corresponding normal devices and the data of the corresponding abnormal devices into the location hazard calculation formula to calculate the location hazard of the corresponding normal devices. The calculation formula for the location hazard of the corresponding normal devices is as follows: Where Qt is the number of abnormal devices near the normal device at time t, Lm is the distance standard value, Ycq is the degree of abnormality of the qth abnormal device near the normal device, Lqt is the distance from the qth abnormal device near time t to the normal device, and dt is the constant of time integration. S5. Based on the results of information hazard analysis and location hazard analysis, assess the equipment transmission hazard and rank the equipment for data acquisition and transmission.

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

3. The microgrid equipment test data acquisition method based on BeiDou positioning as described in claim 2, characterized in that, The equipment transmission hazard assessment based on the information hazard analysis results and the location hazard analysis results includes the following specific steps: The calculated risk level analysis results of the corresponding normal equipment and the location risk level analysis results are obtained, and the equipment transmission risk of the corresponding normal equipment is obtained by weighted summation. The system obtains the degree of abnormality of abnormal devices and the transmission risk of normal devices. Abnormal devices are sorted in descending order of their degree of abnormality, and then the transmission risk of normal devices is sorted in descending order after the abnormal devices to obtain a descending order transmission table. When the data transmission rate meets the data acquisition rate, data is acquired and transmitted according to the order of the descending order transmission table. When the data transmission rate does not meet the data acquisition rate, data is acquired and transmitted from the first few devices in the descending order transmission table.

4. The microgrid equipment test data acquisition method based on BeiDou positioning as described in claim 2, characterized in that, The formula for analyzing the degree of equipment abnormality is: Where T is the monitoring duration of the device, n is the number of types of device operation data, ai is the influence weight of the i-th type of device operation data, xit is the specific value of the i-th type of device operation data at monitoring time t, and xim is the median value of the i-th type of device operation data that needs to be run.

5. The microgrid equipment test data acquisition method based on BeiDou positioning as described in claim 4, characterized in that, The acquisition of operational data, device location, and data transmission between devices during the testing process of each microgrid device includes the following specific steps: S11. Collect the operating data of each device during the test process through the data acquisition terminal. The device operating data is collected through the acquisition terminal. The acquisition terminal has an integrated design that takes into account the collection functions of multiple data. The collected data is stored in the operating data storage module. S12. The location data of the corresponding device is collected in real time by the Beidou satellite positioning module installed on the device and stored in the location data storage module; S13. Obtain the data transmission status between microgrid devices, wherein the data transmission status is represented by the amount of data transmitted, and store the obtained data transmission status in the corresponding storage module.

6. 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 as described in any one of claims 1-5, characterized in that, It specifically includes the following modules: Acquisition module: Acquires operational data, device location, and data transmission between devices during the testing process of each device in the microgrid; Abnormal Equipment Identification Module: Based on the operational data during the testing process of each device in the microgrid, anomaly assessment is performed to identify abnormal devices; Information Risk Analysis Module: Analyzes the information risk level of normal devices based on the data transmission communication between abnormal devices and normal devices; Location Hazard Analysis Module: Analyzes the location hazard level based on the changes in the distance between the abnormal and normal equipment locations; The data acquisition and transmission module assesses the transmission risk of the equipment based on the information risk level analysis results and the location risk level analysis results, and then sorts the equipment data acquisition and transmission based on the transmission risk assessment results.

7. 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 is characterized in that it executes the microgrid equipment test data acquisition method based on BeiDou positioning as described in any one of claims 1-5 by calling a computer program stored in the memory.

8. A computer-readable storage medium, characterized in that, The device stores instructions that, when executed on a computer, cause the computer to perform the microgrid equipment test data acquisition method based on BeiDou positioning as described in any one of claims 1-5.

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