A smart park data security management system based on 5G edge intelligence cooperation

Abstract

This invention belongs to the field of park data security management technology, specifically disclosing a smart park data security management system based on 5G edge intelligent collaboration. The system includes: a park operation analysis module, a park hidden danger confirmation module, a park maintenance judgment module, a park fault confirmation module, a database, and a park security management terminal. This invention identifies fault points in each fault area based on the current at each power detection point, avoiding the current deficiency of not identifying fault points based on the relationship between detection points, thus improving the accuracy of fault location and enabling targeted fault repair measures. Simultaneously, by analyzing the operational safety coefficient of each detection area based on operational data, it overcomes the current problem of insufficient attention to the water supply and gas supply sides, achieving overall park risk assessment and improving the rationality of park resource allocation.

Description

Technical Field

[0001] This invention belongs to the field of park data security management technology, and relates to a smart park data security management system based on 5G edge intelligent collaboration. Background Technology

[0002] With the rapid development of information technology, park management is gradually moving towards intelligence and digitalization. Traditional park management models face many challenges and are unable to meet the needs of modern parks for efficient, safe, and stable operation. In terms of energy management, the collection of energy consumption data such as electricity, water, and gas within the park is untimely and inaccurate, making it impossible to effectively monitor and analyze energy usage and achieve energy conservation and emission reduction goals. For example, the inability to obtain real-time data on electricity, water, and gas consumption in each monitoring area at different times makes it impossible to promptly identify energy waste and take targeted measures. At the same time, there are also shortcomings in park operation safety management. Previous assessments of the operational safety coefficients of various monitoring areas in the park lacked comprehensiveness and real-time accuracy, making it impossible to promptly identify potential risks based on operational data. Therefore, 5G edge intelligent collaborative technology is introduced to manage park data security.

[0003] For example, Chinese invention patent CN118917525A discloses a smart park security management method and system based on data analysis, which relates to the field of security management technology. During operation, sensors are installed at various key locations within the industrial park to collect various security-related data in real time, obtaining a first data group, a second data group, and a third data group. The data is transmitted to a central system via a wireless or wired network. The collected data is stored and preliminarily processed. Based on historical and real-time data, an industrial park security management index Gygl is calculated and compared with preset standard thresholds M and N to assess the security risks of the industrial park. The system monitors various security indicators in real time, identifies abnormal situations, and provides real-time warnings. Various security data are displayed in real time through visualization technology, charts, and dashboards, showcasing the security status of the industrial park and generating security management reports and periodic security reports.

[0004] For example, Chinese invention patent CN119313507A discloses a smart control system for park safety. This invention relates to the field of intelligent control technology. This smart control system for park safety integrates modules for charging area management, data acquisition, charging and environmental analysis, assessment and decision-making, and early warning notification, achieving comprehensive monitoring and management of charging facilities within the park. Through precise charging pile positioning, real-time data acquisition, and multi-level risk assessment, the system can effectively identify and prevent potential safety hazards, ensuring that current, voltage, and environmental parameters during charging remain within safe ranges, greatly improving the operational efficiency and safety of charging facilities. Furthermore, the system possesses intelligent emergency response capabilities and early warning functions. When a risk occurs, the system can automatically take corresponding measures based on the severity of the anomaly, such as reducing charging power or cutting off power, and promptly notify maintenance personnel for handling, guiding personnel to evacuate safely if necessary.

[0005] The existing technologies mentioned above have the following shortcomings: 1. When conducting data security analysis in the park, the current methods mainly rely on real-time monitoring of various security indicators to identify abnormal situations and provide real-time early warnings. However, they do not confirm fault points based on the relationship between detection points, which cannot guarantee the timeliness of fault location. Consequently, they cannot comprehensively assess the security risks of park data, making it difficult to formulate targeted fault repair measures.

[0006] 2. Currently, the security management of park data mainly focuses on the power side, while insufficient attention is paid to the water supply and gas sides. This has led to a lack of overall risk assessment for the park, resulting in an imbalance in the allocation of park resources, which in turn hinders the collaborative management of the park and affects the subsequent construction and innovation of the park. Summary of the Invention

[0007] In view of this, in order to solve the problems mentioned in the background technology, a smart park data security management system based on 5G edge intelligent collaboration is proposed.

[0008] The objective of this invention can be achieved through the following technical solution: This invention provides a smart park data security management system based on 5G edge intelligent collaboration, including: a park operation analysis module, used to acquire operation data of each detection area based on the collaborative technology of 5G communication and edge computing, and then analyze the operation security coefficient of each detection area.

[0009] The park hazard identification module is used to identify areas with potential safety hazards based on the operational safety coefficient of each detection area.

[0010] The park maintenance judgment module is used to extract the repair report type of each detection area, and then judge the maintenance needs of each safety hazard area, and mark the safety hazard area that is judged to need maintenance as the fault area.

[0011] The park fault confirmation module is used to detect the current at each power detection point in each fault area, thereby confirming each fault point in each fault area.

[0012] The database is used to store historical power consumption data for each monitoring area, planned water consumption for each monitoring area during each monitoring period, and power topology map for each monitoring area.

[0013] The park security management terminal is used to manage each fault point in each fault area on the park data management cloud platform.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention confirms each fault point in each fault area based on the current of each fault area at each power detection point, which avoids the current deficiency of not confirming the fault point based on the relationship between the detection points, improves the accuracy of fault location, and thus realizes the targeted formulation of fault maintenance measures.

[0015] (2) This invention analyzes the operational safety coefficient of each detection area based on the operational data of each detection area, which breaks the current problem of insufficient attention to the water supply side and gas side, realizes the overall risk assessment of the park, and improves the rationality of park resource allocation, thereby reducing the obstacles to park collaborative management.

[0016] (3) This invention obtains operational data of each detection area through collaborative technology based on 5G communication and edge computing, which improves the timeliness of data detection, thereby improving the response timeliness of the operational safety factor, and at the same time improving the analysis accuracy of the operational safety factor, reducing the probability of missed judgment and misjudgment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0019] Figure 2 This is a schematic diagram showing the connection steps of the power loss stability coefficient statistical method of the present invention.

[0020] Figure 3 This is a schematic diagram showing the connection steps for confirming each fault point in the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, the present invention provides a smart park data security management system based on 5G edge intelligent collaboration. The system includes: a park operation analysis module, a park hidden danger confirmation module, a park maintenance judgment module, a park fault confirmation module, a database, and a park security management terminal.

[0023] In the above, the park operation analysis module is connected to the park hidden danger confirmation module, the database and the park fault confirmation module respectively. The park maintenance judgment module is connected to the park hidden danger confirmation module and the park fault confirmation module respectively. The park fault confirmation module is also connected to the park operation analysis module, the database and the park safety management terminal respectively.

[0024] The park operation analysis module is used to acquire operation data of each detection area based on the collaborative technology of 5G communication and edge computing, and then analyze the operation safety coefficient of each detection area.

[0025] It should be added that the collaborative technology of 5G communication and edge computing is a comprehensive technology that combines the high speed, low latency, and large capacity of 5G communication technology with the advantages of edge computing in providing computing, storage, and data processing capabilities close to the data source. The 5G network is responsible for quickly transmitting the collected detection data to the edge computing nodes. The edge computing nodes distribute the data according to preset rules. For some data that does not need to be uploaded to the cloud, it is processed and analyzed directly on the local machine, thereby avoiding the transmission of all data to the cloud, reducing the burden on the core network, and improving response speed.

[0026] It should be added that the operational data for each testing area includes: the power factor at each testing time point, the power consumption, water consumption, gas consumption, water pressure at each water supply testing point during each water supply, and gas pressure at each gas supply testing point during each gas supply.

[0027] It should be added that the power consumption of each testing area in each testing time period refers to the total power consumption of each testing area in each testing time period. It is obtained by detecting the power consumption through smart meters installed at the main power inlet of each testing area. The power factor of each testing area at each testing time point is obtained by detecting the power monitoring instrument.

[0028] It should be added that the water consumption of each testing area during each testing period is obtained by smart water meters installed at the main water inlet of each testing area; the gas consumption of each testing area during each testing period is obtained by smart gas meters or gas flow meters installed on the gas pipelines of each testing area; the water pressure at each water supply testing point in each testing area during each water supply is obtained by pressure sensors installed at each water supply testing point; and the gas pressure at each gas supply testing point in each testing area during each gas supply is obtained by pressure sensors installed at each gas supply testing point.

[0029] For example, the analysis of the operational safety factor of each detection area includes: A1, extracting the power consumption of each detection area during each detection period and the power factor at each detection time point from the operational data of each detection area, and then calculating the power stability factor of each detection area. i is the detection area number, i = 1, 2, ..., m.

[0030] Furthermore, the statistical calculation of the power stability coefficient of each detection area includes: A1-1, extracting the power consumption of each detection area in the current detection period from the power consumption of each detection area in each detection period, and then calculating the power loss stability coefficient α of each detection area. i .

[0031] Please see Figure 2 As shown, further, the statistical power loss stability coefficient of each detection area includes: A1-1-1, extracting historical power consumption data of each detection area from the database, and then extracting the power consumption of each detection area in the current detection period of each historical detection date.

[0032] A1-1-2. Using the detection date as the horizontal axis and power consumption as the vertical axis, construct a power consumption change curve for each detection area during the current detection period, and extract the slope of the power consumption change curve as the power consumption change rate for each detection area.

[0033] It should be added that the reason for extracting the slope of the power consumption change curve as the power consumption change rate of each detection area is that the slope of the power consumption change curve intuitively reflects the trend of power consumption change. A positive slope indicates that power consumption is increasing and a negative slope indicates that it is decreasing. This can accurately quantify the degree of change and facilitate the comparison of power consumption changes in different detection areas, thus helping to allocate power resources.

[0034] A1-1-3. Match and compare the power consumption change rate of each detection area with the power consumption change rate interval corresponding to each power loss stability coefficient to obtain the power loss stability coefficient of each detection area, denoted as α. i .

[0035] In one specific embodiment, the power consumption change rate ranges corresponding to each power loss stability coefficient are set as follows: when the power consumption change rate is between -0.1 and 0.1, the corresponding power loss stability coefficient is 0.9; when the power consumption change rate is between 0.1 and 0.5 or between -0.5 and -0.1, the corresponding power loss stability coefficient range is 0.65; and when the power consumption change rate is greater than 0.5 or less than -0.5, the corresponding power loss stability coefficient range is 0.2. If a certain park has detection areas A, B, and C, the specific data is shown in Table 1.

[0036] Table 1. Schematic diagram of power loss stability coefficient in each testing area

[0037]

[0038]

[0039] Based on the matching results of the power consumption change rate and corresponding intervals in each detection area, a power loss stability coefficient for each area is determined. This coefficient measures the stability of power loss; a higher value indicates greater stability. The power consumption change rate in area A is 0.08, falling within the range of -0.1 to 0.1, corresponding to a power loss stability coefficient of 0.9. This indicates high power loss stability in area A, meaning that power consumption changes are relatively stable, the power system's operating state is relatively stable, and various factors affecting power loss are in a relatively balanced state. The power consumption change rate in area B is 0.35, falling within the pre-set range of 0.1 to 0.5. The corresponding power loss stability coefficient in this range is 0.65, indicating that power loss stability in area B is at a moderate level. This suggests that while there are some fluctuations in power loss in area B, they are still within an acceptable range, and the overall operation is relatively stable. The power consumption change rate in area C is -0.6, falling within the range less than -0.5, corresponding to a power loss stability coefficient of only 0.2. This extremely low coefficient fully demonstrates the instability of power loss in area C, and it is highly likely that factors such as equipment aging, abnormal loads, and line faults may affect normal power loss.

[0040] A1-2. Construct a two-dimensional coordinate system with the detection time point as the x-axis and the power factor as the y-axis. Mark several points on the constructed two-dimensional coordinate system for the power factor of each detection area at each detection time point to form a correlation curve. Then, obtain the power factor change threshold for each detection area from the correlation curve, denoted as ψ. i .

[0041] It should be added that the power factor change threshold is obtained as follows: when the absolute value of the slope is large, it indicates that the power factor changes rapidly; when the slope is small, the power factor changes slowly. Therefore, the slope of the correlation curve is selected as the power factor change rate. The slope between each detection time point and its adjacent detection time points is calculated as the power factor change rate at each detection time point. The maximum value is then selected as the power factor change threshold. If the selected maximum value is less than 0, the power factor change threshold is recorded as 0.

[0042] A1-3. Calculate the power factor stability coefficient α for each detection area. i ′, ψ′ is the power factor change value set as a reference.

[0043] It should be added that when the power factor change threshold of the detection area is greater than the set reference power factor change value, the power factor stability coefficient α′=0, meaning that the power factor change in the detection area is large and the stability is poor. When the power factor change threshold of the detection area is less than or equal to the set reference power factor change value, the stability is achieved through… Calculate the power factor stability coefficient α′. The value of α′ is between 0 and 1. The smaller ψ is, the closer α′ is to 1, indicating that the power factor of the detection area changes less and has better stability.

[0044] It should be added that the setting method of ψ′ is as follows: the average value of the power factor change threshold of each detection area is calculated, and the calculation result is used as the power factor change value for setting reference.

[0045] A1-4, from α i and α i The minimum value was selected from the data and used as the power stability coefficient for each detection area.

[0046] It's important to add that power consumption directly reflects the actual electricity demand of each testing area in the park at different times. If the power consumption of a certain testing area fluctuates little across different testing periods, it indicates that the power demand of that testing area is relatively stable, and the power supply can meet its demand relatively smoothly. This is an important indicator of power stability. Conversely, large fluctuations in power consumption indicate frequent equipment start-ups and shutdowns, excessive load changes, etc., which will affect the stability of the power system and increase the uncertainty of power supply. The power factor reflects the ratio of active power to apparent power. It is directly related to the power supply efficiency and voltage stability of the power system. A low power factor means that the reactive power in the circuit is large, which will prevent the power supply equipment from fully utilizing its capacity, increase line losses, and lead to a larger voltage drop on the transmission line, thus affecting the voltage stability at the load end. For example, when a large number of inductive loads (such as motors) are connected to the grid and the power factor is low, the grid voltage will drop, affecting the normal operation of other equipment and reducing the stability of the power system. Power consumption reflects the stability of power demand. Comparing historical data can reveal potential power problems and help assess the system's carrying capacity. Power factor affects power supply efficiency and voltage stability, reflects the operating status of equipment, and facilitates the implementation of optimization measures such as reactive power compensation. Combining the two provides a comprehensive understanding of the health status of the power system. Therefore, power consumption and power factor are chosen for power stability coefficient analysis.

[0047] A2. Extract the water consumption of each testing area during each testing period and the water pressure at each water supply testing point during each water supply from the operational data of each testing area, and calculate the water supply reliability coefficient of each testing area.

[0048] Furthermore, the statistical water supply reliability coefficient of each detection area includes: A2-1, extracting the water consumption of each detection area in the current detection period from the water consumption of each detection area in each detection period.

[0049] A2-2. Extract the planned water consumption of each monitoring area for each monitoring time period from the database, then extract the planned water consumption of each monitoring area for the current monitoring time period, and finally analyze and obtain the water consumption reliability coefficient of each monitoring area using the water consumption reliability analysis formula, denoted as β. i .

[0050] It should be added that the specific formula for reliable water quantity analysis is as follows: p1 and p2 represent the actual water consumption and the planned water consumption, respectively.

[0051] A2-3. Based on the water pressure at each water supply monitoring point during each water supply cycle in each monitoring area, calculate the water pressure reliability coefficient β for each monitoring area. i ′.

[0052] Furthermore, the statistical water pressure reliability coefficient of each detection area includes: A2-3-1, matching and comparing the water pressure of each water supply detection point in each detection area with the set reference water pressure range during each water supply.

[0053] A2-3-2. Count the number of water supply monitoring points in each monitoring area where the water pressure is within the water supply pressure range during each water supply and the total number of water supply monitoring points. Then, use the ratio of the two as the water pressure reliability coefficient of each monitoring area during each water supply.

[0054] A2-3-3. Calculate the average water pressure reliability coefficient for each testing area during each water supply cycle, and use the calculated result as the water pressure reliability coefficient for each testing area, denoted as β. i ′.

[0055] A2-4. Calculate the water supply reliability coefficient for each testing area. y1 and y2 are the weights of the water quantity reliability coefficient and the water pressure reliability coefficient, respectively, y1+y2=1, y1>y2.

[0056] It should be added that, for the park, ensuring a sufficient water supply is the foundation for meeting various production and living activities. If the water supply is insufficient, it will directly lead to production stagnation and inconvenience to life, such as the inability of industrial production to proceed normally and the shortage of water for residents. In comparison, water pressure fluctuations within a certain range may not immediately cause such a serious impact. Therefore, we set y1 > y2. For ease of analysis, y1 can be specifically set to 0.6 and y2 can be specifically set to 0.4.

[0057] In one specific embodiment, it is assumed that a certain park has three detection areas D, E, and F, and the specific data is shown in Table 2.

[0058] Table 2: Calculation Table of Water Supply Reliability Coefficient for Each Monitoring Area in a Certain Industrial Park

[0059]

[0060]

[0061] Calculations show that Zone E has the highest water supply reliability coefficient and the best overall water supply reliability, while Zone F has the lowest water supply reliability coefficient. Further investigation is needed to determine the cause, such as the possibility that some old pipes in Zone F may cause unstable water pressure, or that water-using equipment may have leaks that cause large deviations in water volume, so that targeted measures can be taken to improve water supply reliability.

[0062] A3. Similarly, following the statistical method for the water supply reliability coefficient of each testing area, the gas supply reliability coefficient of each testing area is obtained, denoted as...

[0063] It should be added that the gas supply reliability coefficient of each detection area is based on the gas consumption of each detection area at each detection point and the gas pressure of each gas supply detection point during each gas supply, and is obtained by statistically calculating the water supply reliability coefficient of each detection area in the same way.

[0064] This invention, by analyzing the operational safety coefficient of each detection area based on the operational data of each detection area, overcomes the current problem of insufficient attention to the water supply and gas supply sides, realizes the overall risk assessment of the park, and thus improves the rationality of park resource allocation, thereby reducing the obstacles to collaborative management of the park.

[0065] A4. Calculate the operational safety factor φ for each testing area. i , r1, r2, and r3 are the weights of the power stability coefficient, water supply reliability coefficient, and gas supply reliability coefficient, respectively, set as references. r1 + r2 + r3 = 1, and r1 > r2 > r3.

[0066] It should be added that industrial production is highly dependent on electricity. Many production equipment and processes require a stable power supply to operate normally. Once the power is interrupted or the voltage is unstable, it may lead to damage to production equipment, scrapping of products, or even safety accidents, causing huge economic losses to enterprises. For example, electronic chip manufacturing companies have extremely high requirements for power stability in their production processes. Even a small power fluctuation can affect chip quality. Although industrial production also needs water, except for some industries with special water requirements, such as food processing and chemicals, most industrial enterprises are relatively less dependent on water than on electricity. Water is mainly used for auxiliary processes such as cooling and cleaning. When the water quantity or pressure is insufficient, it generally does not cause serious consequences as immediately as a power outage. It can be dealt with through certain emergency measures or adjustments to production arrangements. In industrial production, natural gas is commonly used for heating, combustion, and other processes. It is relatively easily replaceable, and some companies can choose other energy sources to replace natural gas based on their actual needs, such as electric heating. Moreover, if the natural gas supply is interrupted, companies have some time to adjust their production plans or take emergency measures, so the immediate impact on production is relatively small. Therefore, we set r1 > r2 > r3. For ease of analysis, r1 can be specifically set to 0.5, r2 can be specifically set to 0.3, and r3 can be specifically set to 0.2.

[0067] This invention utilizes collaborative technologies based on 5G communication and edge computing to acquire operational data from each detection area, thereby improving the timeliness of data detection and the responsiveness of the operational safety factor. It also enhances the accuracy of operational safety factor analysis and reduces the probability of missed and false positives.

[0068] The park hazard confirmation module is used to confirm each hazard area based on the operational safety coefficient of each detection area.

[0069] For example, the identification of each potential safety hazard area includes: comparing the operational safety factor of each detection area with a set reference operational safety factor.

[0070] If the operational safety factor of a certain detection area is greater than the set reference operational safety factor, then the detection area is marked as a safety hazard area, and thus each safety hazard area is obtained.

[0071] The park maintenance judgment module is used to extract the repair report type of each detection area, and then judge the maintenance needs of each safety hazard area, and mark the safety hazard area that is judged to need maintenance as a fault area.

[0072] It should be added that the repair types for each testing area are extracted from the park's data management cloud platform.

[0073] For example, determining the maintenance needs of each safety hazard area includes: extracting the maintenance types of each safety hazard area from the maintenance types of each detection area.

[0074] It should be added that the types of repairs reported include: power supply failure, water supply failure, and gas supply failure.

[0075] The power stability coefficient, water supply reliability coefficient, and gas supply reliability coefficient of each safety hazard area are extracted from the power stability coefficient, water supply reliability coefficient, and gas supply reliability coefficient of each detection area, and then compared with the corresponding set reference coefficients to confirm the fault type of each safety hazard area.

[0076] It should be added that the corresponding reference coefficients are the power stability coefficient, water supply reliability coefficient, and gas supply reliability coefficient, respectively. These are obtained by collecting long-term (e.g., 5-10 years) operational data for power, water, and gas in each monitoring area, including the actual values ​​of the power stability coefficient, water supply reliability coefficient, and gas supply reliability coefficient. Statistical analysis is then performed on this data to calculate the average and standard deviation. For example, if the average power stability coefficient over the past few years is 0.92 and the standard deviation is 0.03, then the average power stability coefficient of 0.92 is used as the reference coefficient. The average is then added to the standard deviation by a certain multiple, such as 0.92 + 0.03 = 0.95, to obtain the power stability coefficient. The methods for obtaining the water supply reliability coefficient and gas supply reliability coefficient are similar and will not be repeated here.

[0077] It should be added that the process for confirming the fault type of each safety hazard area is as follows: if the power stability coefficient of the safety hazard area is less than the set reference power stability coefficient, then the fault type of the safety hazard area is recorded as a power fault.

[0078] If the water supply reliability coefficient of the area with safety hazards is less than the set reference water supply reliability coefficient, then the fault type of the area with safety hazards will be recorded as a water supply fault.

[0079] If the gas supply reliability coefficient of a safety hazard area is less than the set reference gas supply reliability coefficient, then the fault type of the safety hazard area is recorded as a gas supply fault, and thus the fault type of each safety hazard area is obtained.

[0080] The types of repair requests and fault types in each area with potential safety hazards are matched and compared.

[0081] When the reported repair type and the fault type of a safety hazard area are the same, the repair requirement of the safety hazard area is recorded as no repair needed; otherwise, the repair requirement of the safety hazard area is recorded as repair needed, thus obtaining the repair requirements of each safety hazard area.

[0082] The park fault confirmation module is used to detect the current at each power detection point in each fault area, thereby confirming each fault point in each fault area.

[0083] It should be added that the current in each fault area at each power detection point is obtained by current transformers installed on the power lines.

[0084] Please see Figure 3 As shown, by way of example, the step of confirming each fault point in each fault area includes: S1, extracting the fault type of each fault area from the fault types of each safety hazard area.

[0085] S2. When the fault type of the fault area is a power fault, based on the current of each fault area at each power detection point, count the fault points in the fault area when the fault type is a power fault.

[0086] Furthermore, the fault points in the statistical fault area when the fault type is a power fault include: S2-1, for each power detection point in the fault area, matching and comparing its current value with a pre-set reference permissible current range; if the current value exceeds the permissible current range, then the detection point is determined to be a power detection abnormal point.

[0087] S2-2. After detecting an abnormal power detection point, further clarify the power fault type, which is either too high current or too low current.

[0088] It should be added that the process for further clarifying the type of power fault is as follows: if the current value is greater than the upper limit of the set reference permissible current range, the power fault type is marked as overcurrent; if the current value is less than the lower limit of the set reference permissible current range, the power fault type is marked as undercurrent.

[0089] S2-3. Extract the power topology map of each detection area from the database, and then locate the fault point to obtain the fault points in the fault area when the fault type is power fault.

[0090] It should be added that the fault location process is as follows: If the abnormal power detection point is excessive current, start from the power detection point where excessive current is detected, and check each branch line step by step along the current flow direction on the power topology map. For each branch line, according to Kirchhoff's current law, under normal circumstances, the current flowing into the node is equal to the current flowing out of the node. When it is found that the current flowing into a certain node is much greater than the current flowing out, it indicates that there may be a short circuit fault in the line downstream of that node, and the downstream line is marked as the key inspection target. For example, in a simple T-shaped power topology, a high current anomaly is detected at point A. Point A connects to two branch lines, B and C. Measurements show that the current flowing into point A is 100A, while the current flowing out of line B is 30A and the current flowing out of line C is 20A. Further investigation is needed in the downstream areas where lines B and C are located. Voltage measurements are taken at several key nodes. Under normal circumstances, the voltage drop between nodes should conform to the characteristics of the line and the load. When a short circuit fault exists, the voltage across the short circuit point will drop sharply. For example, if the voltage between two adjacent nodes is almost zero in a certain section of line C, while this voltage value should normally be maintained within a certain range, then the line between the two nodes is considered the fault point.

[0091] It should be added that the selection of critical nodes should prioritize the midpoint of a longer line segment, nodes connecting important loads, and nodes located at critical branches in the power topology diagram.

[0092] If the abnormal power detection point is low current, trace back along the power topology diagram from the point of low current detection, moving against the current direction. Since low current may be caused by an open circuit, focus on the connection points and branches of the line. When a line segment is found to have no current flowing through it (detected current is 0 or far below the normal range), and its upstream line has normal current flowing in, then it is determined that there is an open circuit fault in that line segment, and this line segment is marked as the fault point. For example, in a series power topology line, if a low current anomaly occurs at detection point D, tracing upstream from point D, it is found that the current in the line between point C and point D is 0, while the current upstream of point C is normal. Therefore, the line segment CD is designated as the fault point.

[0093] S3. Extract the water pressure at each water supply detection point and the gas pressure at each gas supply detection point in each fault area during the current water supply period from the operation data of each detection area.

[0094] S4. Similarly, according to the statistical method of each fault point when the fault type is power failure, the fault points when the fault types are water supply failure and gas failure are obtained, and then the fault points of each fault area are obtained.

[0095] This invention, by identifying each fault point in each fault area based on the current at each power detection point, avoids the current shortcomings of not identifying fault points based on the relationship between detection points, improves the accuracy of fault location, and thus enables the targeted formulation of fault repair measures.

[0096] The database is used to store historical power consumption data for each detection area, planned water consumption for each detection area during each detection period, and power topology map of each detection area.

[0097] The park security management terminal is used to manage each fault point in each fault area on the park data management cloud platform.

[0098] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A smart park data security management system based on 5G edge intelligent collaboration, characterized in that: The system includes: The park operation analysis module is used to acquire operation data of each detection area based on the collaborative technology of 5G communication and edge computing, and then analyze the operation safety coefficient of each detection area. The park hazard identification module is used to identify areas with potential safety hazards based on the operational safety coefficient of each detection area. The park maintenance judgment module is used to extract the repair report type of each detection area, and then judge the maintenance needs of each safety hazard area, and mark the safety hazard area that is judged to need maintenance as the fault area; The park fault confirmation module is used to detect the current at each power detection point in each fault area, thereby confirming each fault point in each fault area. The database is used to store historical power consumption data for each detection area, the planned water consumption for each detection area during each detection period, and the power topology map for each detection area. The park security management terminal is used to manage each fault point in each fault area on the park data management cloud platform.

2. The smart park data security management system based on 5G edge intelligent collaboration according to claim 1, characterized in that: The analysis of the operational safety factor of each detection area includes: A1. Extract the power consumption and power factor of each testing area during each testing period from the operational data of each testing area, and then calculate the power stability coefficient of each testing area. i is the detection region number, i = 1, 2, ..., m; A2. Extract the water consumption of each testing area during each testing period and the water pressure at each water supply testing point during each water supply from the operational data of each testing area, and calculate the water supply reliability coefficient of each testing area. A3. Similarly, following the statistical method for the water supply reliability coefficient of each testing area, the gas supply reliability coefficient of each testing area is obtained, denoted as... A4. Calculate the operational safety factor φ for each testing area. i , r1, r2, and r3 are the weights of the power stability coefficient, water supply reliability coefficient, and gas supply reliability coefficient, respectively, set as references. r1 + r2 + r3 = 1, and r1 > r2 > r3.

3. The smart park data security management system based on 5G edge intelligent collaboration according to claim 2, characterized in that: The statistical power stability coefficients for each detection area include: The power consumption of each detection area during the current detection period is extracted from the power consumption of each detection area during each detection period, and then the power loss stability coefficient α of each detection area is calculated. i ; A two-dimensional coordinate system is constructed with the detection time point as the x-axis and the power factor as the y-axis. Several points are then marked on the constructed two-dimensional coordinate system for the power factor of each detection area at each detection time point, forming a correlation curve. The power factor change threshold for each detection area is then obtained from the correlation curve, denoted as ψ. i ; Statistical analysis of the power factor stability coefficient α for each detection area i ′, ψ′ is the power factor change value set as a reference; From α i and α i The minimum value was selected from the data and used as the power stability coefficient for each detection area.

4. The smart park data security management system based on 5G edge intelligent collaboration according to claim 2, characterized in that: The statistical power loss stability coefficients for each detection area include: Historical power consumption data for each detection area is extracted from the database, and then the power consumption of each detection area in each detection period on each historical detection date is extracted. With the detection date as the horizontal axis and power consumption as the vertical axis, construct the power consumption change curve for each detection area in each detection time period, and extract the slope of the power consumption change curve as the power consumption change rate for each detection area. The power consumption change rate of each detection area is matched and compared with the power consumption change rate range corresponding to each power loss stability coefficient to obtain the power loss stability coefficient of each detection area, denoted as α. i .

5. A smart park data security management system based on 5G edge intelligent collaboration as described in claim 2, characterized in that: The statistical analysis of water supply reliability coefficients for each testing area includes: Extract the water consumption of each testing area in the current testing period from the water consumption of each testing area in each testing time period; The planned water consumption for each monitoring area during each monitoring period is extracted from the database. Then, the planned water consumption for each monitoring area during the current monitoring period is extracted. Finally, the water consumption reliability coefficient for each monitoring area is obtained using the water consumption reliability analysis formula, denoted as β. i ; Based on the water pressure at each water supply monitoring point during each water supply period in each monitoring area, the water pressure reliability coefficient β for each monitoring area is calculated. i ′; Statistical analysis of water supply reliability coefficients in each testing area y1 and y2 are the weights of the water quantity reliability coefficient and the water pressure reliability coefficient, respectively, y1+y2=1, y1>y2.

6. A smart park data security management system based on 5G edge intelligent collaboration as described in claim 5, characterized in that: The statistical analysis of the water pressure reliability coefficient for each detection area includes: The water pressure at each water supply testing point in each testing area during each water supply is matched and compared with the set reference water pressure range; The number of water supply monitoring points in each monitoring area whose water pressure is within the water supply pressure range during each water supply and the total number of water supply monitoring points are counted, and then the ratio of the two is used as the water pressure reliability coefficient of each monitoring area during each water supply. The average water pressure reliability coefficient of each testing area during each water supply is calculated, and the result is used as the water pressure reliability coefficient of each testing area, denoted as β. i ′.

7. A smart park data security management system based on 5G edge intelligent collaboration as described in claim 2, characterized in that: The identified areas of potential safety hazards include: The operational safety factor of each detection area is compared with the set reference operational safety factor; If the operational safety factor of a certain detection area is greater than the set reference operational safety factor, then the detection area is marked as a safety hazard area, and thus each safety hazard area is obtained.

8. A smart park data security management system based on 5G edge intelligent collaboration as described in claim 7, characterized in that: The determination of maintenance needs for each safety hazard area includes: Extract the repair types from each inspection area, and then extract the repair types from each area with safety hazards. The power stability coefficient, water supply reliability coefficient, and gas supply reliability coefficient of each safety hazard area are extracted from the power stability coefficient, water supply reliability coefficient, and gas supply reliability coefficient of each detection area, and then compared with the corresponding set reference coefficients to confirm the fault type of each safety hazard area. Match and compare the reported repair types and fault types for each area with potential safety hazards; When the reported repair type and the fault type of a safety hazard area are the same, the repair requirement of the safety hazard area is recorded as no repair needed; otherwise, the repair requirement of the safety hazard area is recorded as repair needed, thus obtaining the repair requirements of each safety hazard area.

9. A smart park data security management system based on 5G edge intelligent collaboration as described in claim 8, characterized in that: The confirmation of each fault point in each fault area includes: S1. Extract the fault types of each fault area from the fault types of each safety hazard area; S2. When the fault type of the fault area is a power fault, based on the current of each fault area at each power detection point, count the fault points in the fault area when the fault type is a power fault. S3. Extract the water pressure at each water supply detection point and the gas pressure at each gas supply detection point in each fault area during the current water supply and the gas supply detection point during the current gas supply from the operational data of each detection area. S4. Similarly, based on the statistical method for each fault point when the fault type is power failure, the fault points when the fault type is water supply failure and gas supply failure are obtained, and then the fault points in each fault area are obtained.

10. A smart park data security management system based on 5G edge intelligent collaboration as described in claim 9, characterized in that: The fault points in the statistical fault area when the fault type is a power fault include: For each power detection point in the fault area, its current value is matched and compared with a pre-set reference permissible current range. If the current value exceeds the permissible current range, it is determined to be a power detection anomaly point. Once an abnormal power detection point is detected, the power fault type is further identified as either too high current or too low current. The power topology map of each detection area is extracted from the database, and then the fault point is located to obtain the fault points in the fault area when the fault type is power fault.

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