Electric power communication prevention system based on Beidou and optical fiber sensing

Abstract

The invention relates to an electric power communication prevention system based on Beidou and optical fiber sensing, belongs to the technical field of electric power communication, and is used for solving the problem that positioning points of communication interruption, delay or misinformation in a high-attention area are easily ignored. The method comprises the following steps: acquiring a regional concern point number difference value, a regional concern level change value, a regional highest temperature and lowest temperature difference change value, a regional concern point strain value average difference and a vibration risk coefficient to obtain a regional current monitoring coefficient and a regional actual risk coefficient, and formulating a group of fuzzy rules according to the regional current monitoring coefficient and the regional actual risk coefficient to perform fuzzy reasoning. The method comprises the following steps: determining a focus point adjustment ratio result of a prevention area, establishing a time sequence model for prediction, determining a prevention area range distribution scheme according to a prediction result, reducing monitoring interference in the area, and timely discovering a positioning point with communication interruption, delay or misinformation in the prevention area.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electric power communication, and particularly relates to an electric power communication prevention system based on Beidou and optical fiber sensing. BACKGROUND

[0002] With the rapid development of modern electric power systems, electric power communication, as an important supporting technology for stable operation of electric power systems, has become a core component of power grid dispatching, operation monitoring and fault handling. Through Beidou technology, high-precision positioning and time high-synchronization are provided, and high-sensitivity detection of physical parameters by optical fibers enables the electric power communication system to timely find the positioning points of communication interruption, delay or false report in actual operation, thereby ensuring the safe and stable operation of the power grid.

[0003] The prior art has the following disadvantages:

[0004] At present, the state monitoring of the current electric power communication line mainly relies on discrete monitoring points, and cannot achieve centralized management and control and dynamic adjustment of preventive measures, resulting in that there is no characteristic analysis in each region. Specifically, the attention degree of Beidou satellites to each region and the real-time monitoring of optical fiber sensing cannot be dynamically adjusted, which causes resource waste and easily ignores the positioning points of communication interruption, delay or false report in high-attention regions. Therefore, an electric power communication prevention system based on Beidou and optical fiber sensing is proposed. SUMMARY

[0005] The purpose of the present application is to overcome the above-mentioned defects of the prior art and provide an electric power communication prevention system based on Beidou and optical fiber sensing.

[0006] To achieve the above-mentioned purpose, the technical scheme of the present application is as follows: an electric power communication prevention system based on Beidou and optical fiber sensing, comprising a data acquisition module, a data processing module, a supplementary evaluation module and a range division module, and the modules are signal-connected;

[0007] The data acquisition module is used for acquiring Beidou satellite feature information and optical fiber sensor feature information, obtaining a regional attention point quantity difference value, a regional attention level change value, a regional maximum temperature and minimum temperature difference change value, a regional attention point strain value average difference and a vibration risk coefficient through data processing, and sending them to the data processing module;

[0008] The data processing module is used for obtaining the regional attention point quantity difference value, the regional attention level change value, the regional maximum temperature and minimum temperature difference change value, the regional attention point strain value average difference and the vibration risk coefficient, establishing a data analysis model, obtaining a regional current monitoring coefficient and a regional actual risk coefficient, and sending them to the supplementary evaluation module;

[0009] The supplementary evaluation module is configured to obtain the current monitoring coefficient of the region and the actual risk coefficient of the region, and input the same into the fuzzy logic for fuzzy reasoning to determine the attention point adjustment ratio result of the prevention region and send the same to the range division module.

[0010] The range division module is configured to obtain the attention point adjustment ratio result of the prevention region, perform range allocation prediction analysis through a time series model, and further refine and optimize the adjustment of the range allocation scheme of the prevention region.

[0011] Further, the Beidou satellite feature information includes a regional attention point number difference value and a regional attention level change value; the fiber sensor feature information includes a regional maximum temperature and minimum temperature difference change value, a regional attention point strain value average difference, and a vibration risk coefficient.

[0012] Further, the regional attention point number difference value, the regional attention level change value, the regional maximum temperature and minimum temperature difference change value, the regional attention point strain value average difference, and the vibration risk coefficient are obtained in the following manner:

[0013] The number of attention points in the current unit time is defined and counted in priority, and the size relationship with the number of attention points in the previous unit time is analyzed, and then the difference value is calculated to obtain the regional attention point number difference value Wherein, i is the i-th unit time, and g is the g-th prevention region.

[0014] The coverage range of all attention points in the region and the weighted average calculation of the interaction frequency of all attention points are collected, and the current regional attention level is obtained by substituting the preset attention level, and the regional attention level change value is obtained by subtracting the regional attention level of the previous unit time

[0015] The maximum temperature power communication node and the minimum temperature power communication node in the region in the current unit time are queried, and the difference between the maximum temperature and the minimum temperature in the region is calculated, and the difference between the maximum temperature and the minimum temperature in the region in the previous unit time is calculated to obtain the regional maximum temperature and minimum temperature difference change value

[0016] The strain values of all attention points in the region are monitored, the strain values of each attention point in the current unit time and the previous unit time are recorded, and the difference between the strain values of each attention point is calculated, and the average of the regional attention point strain value difference is calculated to obtain the regional attention point strain value average difference

[0017] The amplitude and frequency of the vibration of the prevention region are collected in real time to determine the maximum vibration amplitude of the equipment and the critical frequency value of the equipment loss, and the weighted calculation is performed to obtain the vibration risk coefficient

[0018] Further, the data analysis model is established by the area focus point quantity difference value, the area focus level change value, the area highest temperature and lowest temperature difference change value, the area focus point strain value average difference and the vibration risk coefficient to generate the area current monitoring coefficient and the area actual risk coefficient The formula is:

[0019]

[0020] In the formula, is the area current monitoring coefficient, is the area actual risk coefficient, ω1, ω2, ω3, ω4 and ω5 are respectively preset proportion coefficients of the area focus point quantity difference value, the area focus level change value, the area highest temperature and lowest temperature difference change value, the area focus point strain value average difference and the vibration risk coefficient, and ω1, ω2, ω3, ω4 and ω5 are all greater than 0.

[0021] Further, the area current monitoring coefficient and the area actual risk coefficient are obtained, substituted into the fuzzy logic for fuzzy reasoning, and the focus point adjustment ratio result of the prevention area is determined, and the specific implementation manner is:

[0022] The area current monitoring coefficient and the area actual risk coefficient are defined as input variables, which are respectively divided into different fuzzy sets;

[0023] The focus point adjustment ratio result of the prevention area is defined as an output variable, which is divided into a fuzzy set;

[0024] Fuzzy rules are made to describe the influence of the area current monitoring coefficient and the area actual risk coefficient on the focus point adjustment ratio result of the prevention area;

[0025] According to the fuzzy rules, the focus point adjustment ratio result of the prevention area is determined.

[0026] Further, the focus point adjustment ratio result of the prevention area includes the focus point adjustment ratio of each prevention area.

[0027] Further, the time series model adopts an ARIMAX model.

[0028] Further, the specific steps of the range allocation prediction analysis by the time series model are as follows:

[0029] Step A1, obtaining prediction data;

[0030] Step A2, establishing an ARIMAX model;

[0031] Step A3, the ARIMAX model parameters are estimated by using the maximum likelihood estimation (MLE) method;

[0032] Step A4, the effect of the fitted model is verified, and the goodness of fit of the model is checked by residual analysis method;

[0033] Step A5, the fitted model is used to predict the future allocation scheme, and the maximum value of the prediction result is taken as the highest point of the range division in the future time period.

[0034] Further, the exogenous variables in the ARIMAX model include the regional attention point number difference, the regional attention level change value, the regional maximum temperature and minimum temperature difference change value, the regional attention point strain value average difference and the vibration risk coefficient, and the basic form of the ARIMAX model is:

[0035]

[0036] In the formula, y t is the range division in the current unit time, y t-i is the range division lagging i steps, alpha is a constant term, is the i-th order autoregressive parameter, p is the order of the autoregressive term, theta j is the j-th order moving average parameter, q is the order of the moving average term, epsilon t-j is the white noise term lagging j steps, epsilon t is the white noise term, which represents random error, beta k is the coefficient of the exogenous variable X t-k , m is the lag order of the exogenous variable, X t-k is the exogenous variable lagging k steps.

[0037] Further, alpha, theta j , beta k are calculated by the maximum likelihood estimation method.

[0038] Compared with the prior art, the present application has the following beneficial effects:

[0039] 1、The present application collects the regional attention point number difference, the regional attention level change value, the regional maximum temperature and minimum temperature difference change value, the regional attention point strain value average difference and the vibration risk coefficient to obtain the regional current monitoring coefficient and the regional actual risk coefficient, formulates a set of fuzzy rules for fuzzy reasoning according to the regional current monitoring coefficient and the regional actual risk coefficient, determines the attention point adjustment ratio result of the prevention area, and adjusts the number and attention level of the attention points in the prevention area, so that the risk of the prevention area of the power communication is prevented in advance, resource waste is reduced, and the positioning point of communication interruption, delay or false alarm in the prevention area is found in time.

[0040] 2、The application obtains the attention point adjustment ratio result of the prevention area, establishes a time series model for prediction, determines the prevention area range distribution scheme according to the prediction result, reduces the mutual coverage probability of the attention points of each prevention area, improves the monitoring resource utilization efficiency, and reduces the monitoring interference in the area. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 It is a module schematic diagram of the power communication prevention system based on Beidou and optical fiber sensing. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0043] The disclosure preferably presets a plurality of prevention areas, and the setting and number of specific prevention areas are constructed by the experimenters according to historical detection factors, historical communication interruption times and the like.

[0044] The application obtains the current monitoring coefficient of the area and the actual risk coefficient of the area by weighted calculation according to the Beidou satellite feature information and the optical fiber sensor feature information, performs fuzzy reasoning according to fuzzy logic, improves the attention points of part of the prevention areas, and especially uses the time series model to analyze the number of corresponding attention points added by the Beidou satellite in the surrounding area of the attention point.

[0045] Embodiment 1

[0046] The present example provides a power communication prevention system based on Beidou and optical fiber sensing, as shown in the figure, comprising a data acquisition module, a data processing module, a supplementary evaluation module and a range division module; the modules are signal connected; Figure 1

[0047] The data acquisition module is used for acquiring the Beidou satellite feature information and the optical fiber sensor feature information, obtaining the regional attention point number difference, the regional attention level change value, the regional highest temperature and lowest temperature difference change value, the regional attention point strain value average difference and the vibration risk coefficient through data processing, and sending to the data processing module;

[0048] ​Specifically, the data processing includes data type conversion, which refers to converting the original data collected by the system into a standardized data type, missing value processing, which refers to filling in missing data that may occur during system collection through interpolation, mean filling, to ensure data continuity and integrity, etc. The purpose of data processing is to make the data clearer and easier to understand, and to make the system calculation more rapid.

[0049] Specifically, the above data operation methods are prior art and will not be described here.

[0050] The Beidou satellite feature information includes regional focus point quantity difference and regional focus level change value; the optical fiber sensor feature information includes regional maximum temperature and minimum temperature difference change value, regional focus point strain value average difference, and vibration risk coefficient.

[0051] The regional focus point quantity difference refers to the change amount of the number of focus points in different unit times in the prevention area. The acquisition logic is to define and count the number of focus points in the current unit time first, analyze the size relationship with the number of focus points in the last unit time, and then calculate the difference to obtain the regional focus point quantity difference. Wherein, i is the i-th unit time, and g is the g-th prevention area.

[0052] It should be noted that the focus point refers to an abnormal or potential risk point that needs to be monitored, such as temperature focus point, strain focus point, vibration focus point, and environment focus point, etc. The region is a plurality of prevention areas set by the system, which will not be described here.

[0053] Further, the setting and deletion of focus points can be set or deleted according to the preset threshold value of each focus point or the system according to the replacement of the prevention area, etc. which will not be described here.

[0054] The regional focus level change value refers to the change amount of the comprehensive focus level of the prevention area in the unit time, which is used to reflect the dynamic change of the monitoring focus level of the region. The acquisition logic is to collect the coverage of all focus points in the region and the interaction frequency of all focus points, and then calculate the weighted average, and substitute it into the preset focus level to obtain the current regional focus level. The regional focus level change value is obtained by subtracting the regional focus level of the last unit time.

[0055] It should be noted that the attention level refers to the result L calculated by the experimenters in this example based on the weighted average of the coverage of all attention points in multiple areas and the interaction frequency of all attention points. Multiple attention level indicators are set. For example, L below 10 is low risk, L between 10 and 20 is medium risk, and L above 20 is high risk, etc. The specific attention level label can be multiple levels. This example is only used as an example and will not be elaborated here.

[0056] The change in the difference between the highest and lowest temperatures in a region refers to the change in the difference between the highest and lowest temperatures within a unit of time. It is used to measure the changing trend of the dynamic characteristics of regional temperature distribution. The logic for obtaining this value is as follows: query the power communication nodes with the highest and lowest temperatures in the region at the current unit of time, subtract them to calculate the difference between the highest and lowest temperatures, and then calculate the difference between this difference and the difference in the highest and lowest temperatures at the previous unit of time to obtain the change in the difference between the highest and lowest temperatures in the region.

[0057] It should be noted that the variation values ​​of the difference between the highest and lowest temperatures in the region, the average difference of strain values ​​at points of interest in the region, and the vibration risk coefficient were all obtained through fiber optic sensing technology. Specifically, fiber optic sensing technology utilizes the high sensitivity of optical fibers to detect physical parameters (such as temperature, vibration, strain, etc.) and obtains the results through Bragg gratings.

[0058] A Bragg grating is a special type of fiber optic sensor that forms a periodically varying refractive index structure within the fiber. Incident light undergoes selective reflection within this grating; light of a specific wavelength satisfying the Bragg condition is reflected. This wavelength is called the Bragg wavelength, and its specific formula is as follows:

[0059] λ B =2nΛ

[0060] In the formula, n is the effective refractive index of the optical fiber, Λ is the grating period, and λ is the reticle period. B For the Bragg wavelength;

[0061] Among them, the Bragg wavelength is very sensitive to the strain and temperature of the optical fiber. Strain will change the spacing and refractive index of the grating, while temperature will cause the grating to expand thermally and change its refractive index.

[0062] Furthermore, the difference between the highest and lowest temperatures in the aforementioned region will cause changes in the refractive index and grating period, and the Bragg wavelength will also shift accordingly. The specific formula is as follows:

[0063] Δλ B =λ B (α+ξ)ΔT

[0064] Wherein, a is the thermal expansion coefficient, ξ is the thermo-optic coefficient, ΔT is the temperature change value, Δλ B is the Bragg wavelength change value;

[0065] The regional focus point strain value average difference refers to the average value of the strain value difference of all focus points in the prevention area per unit time, which is used to measure the overall trend and distribution characteristics of the strain change in the region. The acquisition logic is to monitor the strain values of all focus points in the region, record the strain values of each focus point in the current unit time and the last unit time, and perform subtraction calculation to obtain the strain value difference of each focus point, and then perform average calculation to obtain the regional focus point strain value average difference

[0066] It should be noted that when the optical fiber is stretched or compressed, the period and refractive index of the grating will change, resulting in a shift in the Bragg wavelength. The specific formula is expressed as:

[0067] Δλ B = λ B (1-P e )ε

[0068] Wherein, P e is the photoelastic effect coefficient, ε is the strain value, Δλ B is the Bragg wavelength change value;

[0069] The vibration risk coefficient refers to an index that quantifies the impact of vibration on the safety of the power communication system, which comprehensively considers the amplitude and frequency of vibration to assess the actual risk that may be caused by external interference or internal equipment failure. The acquisition logic is to collect the amplitude and frequency of vibration in the prevention area in real time, determine the maximum vibration amplitude of the equipment and the critical frequency value of equipment loss, and perform weighted calculation to obtain the vibration risk coefficient

[0070] Wherein, the weighted formula of the vibration risk coefficient is expressed as:

[0071]

[0072] Wherein, is the vibration risk coefficient, β is the vibration risk weight coefficient, which represents the importance of vibration risk to the overall system safety, and is usually determined by empirical value or historical data, A i is the vibration amplitude of the current unit time, A max is the maximum vibration amplitude, f i is the vibration frequency of the current unit time, f max is the critical frequency value of equipment loss;

[0073] It should be noted that the maximum vibration amplitude of the device and the critical frequency value of the device loss are obtained by comprehensively calculating the vibration amplitude and frequency exceeding the preset threshold value in history, and the specific comprehensive calculation method is not limited, but is set by the experiment personnel according to the number of vibration amplitude and frequency exceeding the preset threshold value, which will not be repeated here.

[0074] The data processing module is used to obtain the regional focus point quantity difference, the regional focus level change value, the regional maximum temperature and minimum temperature difference change value, the regional focus point strain value average difference and the vibration risk coefficient, establish a data analysis model, obtain the regional current monitoring coefficient and the regional actual risk coefficient, and send to the supplementary evaluation module.

[0075] The data analysis model refers to a weighted analysis model, which generates the regional current monitoring coefficient and the regional actual risk coefficient through weighted calculation.

[0076] The data analysis model refers to a weighted analysis model, which generates the regional current monitoring coefficient and the regional actual risk coefficient through weighted calculation. The formula is:

[0077]

[0078] In the formula, is the regional current monitoring coefficient, is the regional actual risk coefficient, ω1, ω2, ω3, ω4 and ω5 are respectively the preset proportion coefficients of the regional focus point quantity difference, the regional focus level change value, the regional maximum temperature and minimum temperature difference change value, the regional focus point strain value average difference and the vibration risk coefficient, and ω1, ω2, ω3, ω4 and ω5 are all greater than 0.

[0079] The regional focus point quantity difference, the regional focus level change value, the regional maximum temperature and minimum temperature difference change value, the regional focus point strain value average difference and the vibration risk coefficient are all directly expressed as data reflecting the saturation or vacancy of the focus point in the prevention area in the current unit time.

[0080] As can be seen from the formula, the higher the regional focus point quantity difference, the regional focus level change value, the regional maximum temperature and minimum temperature difference change value, the regional focus point strain value average difference and the vibration risk coefficient, the more vacancy of the focus point in the prevention area in the current unit time, and the higher the regional current monitoring coefficient and the regional actual risk coefficient, and vice versa, the more saturation of the focus point in the prevention area in the current unit time, and the lower the regional current monitoring coefficient and the regional actual risk coefficient. ​

[0081] The supplementary evaluation module is used to obtain the current monitoring coefficient of the region and the actual risk coefficient of the region, and substitute them into the fuzzy logic for fuzzy reasoning to determine the attention point adjustment ratio result of the prevention region and occur to the range division module;

[0082] For example, "High", "Low", "Medium" for the current monitoring coefficient of the region and the actual risk coefficient of the region;

[0083] A set of fuzzy rules are formulated to describe the influence of different input variables on the output variable. The definition of the rule can be based on professional knowledge, or obtained through data analysis and experiment. For example:

[0084] The current monitoring coefficient of the region is marked as X, the actual risk coefficient of the region is marked as U, and the attention point adjustment ratio result of the prevention region is marked as T_height;

[0085] Then it can be defined as:

[0086] Rule 1: IF (X is High) AND (U is High) THEN (T_height is High)

[0087] Rule 2: IF (X is Low) AND (U is Low) THEN (T_height is Low)

[0088] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although this embodiment takes three fuzzy sets as an example, the current monitoring coefficient of the region and the actual risk coefficient of the region can be divided into more than three sets to facilitate better precision adjustment according to different adjustment ratios.

[0089] Further, the judgment of high, medium and low of the current monitoring coefficient of the region and the actual risk coefficient of the region can be set according to the actual situation Threshold judgment, for example, the value of the current monitoring coefficient of the region is more than 75% of the total system, it is marked as "High", the value of the actual risk coefficient of the region is less than 34% of the total system, it is marked as "Low", etc. Here is not repeated.

[0090] The attention point adjustment ratio of the prevention region is counted as "High", and the number and level of the attention points of the prevention region are adjusted according to the size of the attention point adjustment ratio of the prevention region. The specific adjustment range is the specific way implemented by the experiment personnel according to the specific demand, which is not limited here;

[0091] The application obtains the current monitoring coefficient of the region and the actual risk coefficient of the region by collecting the regional focus point quantity difference, the regional focus level change value, the regional maximum temperature and minimum temperature difference change value, the regional focus point strain value average difference and the vibration risk coefficient, formulates a set of fuzzy rules for fuzzy reasoning according to the current monitoring coefficient of the region and the actual risk coefficient of the region, determines the focus point adjustment ratio result of the prevention region, and adjusts the focus point quantity and the focus level of the prevention region, so that the risk of the prevention region of the power communication is prevented in advance, resource waste is reduced, and the positioning point of the communication interruption, delay or false alarm in the prevention region is found in time.

[0092] Embodiment 2

[0093] The operation strategy of collecting the regional focus point quantity difference, the regional focus level change value, the regional maximum temperature and minimum temperature difference change value, the regional focus point strain value average difference and the vibration risk coefficient, obtaining the current monitoring coefficient of the region and the actual risk coefficient of the region, formulating a set of fuzzy rules for fuzzy reasoning, and determining the focus point adjustment ratio result of the prevention region is mainly illustrated in Embodiment 1 of the application; however, only the number of focus points in the prevention region is adjusted in Embodiment 1, and the range of the prevention region is increased or decreased to meet the supplement or screening of the focus points, but the adjustment range of the prevention region is not limited, so that the focus points in other prevention ranges are covered, thereby reducing the credibility of the prevention region; in view of the above problems, Embodiment 2 of the application is further refined;

[0094] The range division module is used for obtaining the focus point adjustment ratio result of the prevention region, performing range allocation prediction analysis through the time series model, and further refining and optimizing the adjustment of the prevention region range allocation scheme;

[0095] The focus point adjustment ratio result of the prevention region includes the focus point adjustment ratio of each prevention region;

[0096] It should be noted that the time series model in this embodiment is an ARIMAX model, and the refinement and optimization of the adjustment of the prevention region range allocation scheme means that, under the condition of determining the focus point adjustment ratio of each prevention region, the prevention region range allocation scheme is further analyzed and adjusted to be more scientific and reasonable, so as to realize the control of the prevention region range;

[0097] Further, the specific steps of performing range allocation prediction analysis through the time series model are as follows:

[0098] Step A1, obtaining prediction data;

[0099] Step A2, establishing an ARIMAX model;

[0100] Step A3, using the maximum likelihood estimation (MLE) method to estimate the ARIMAX model parameters;

[0101] Step A4, verifying the effect of the fitted model, checking the goodness of fit of the model by residual analysis method;

[0102] Step A5, using the fitted model to predict the future allocation scheme, taking the maximum value of the prediction result as the highest point of the range division in the future time period;

[0103] Specifically, the prediction data includes the regional attention point quantity difference corresponding to each unit time, the regional attention level change value, the regional maximum temperature and minimum temperature difference change value, the regional attention point strain value average difference, the vibration risk coefficient and the range division data; wherein the regional attention point quantity difference corresponding to each unit time, the regional attention level change value, the regional maximum temperature and minimum temperature difference change value, the regional attention point strain value average difference and the vibration risk coefficient have been exemplified in Embodiment 1 and will not be repeated here;

[0104] The range division data refers to the historical range division data, which is taken as the main variable of the time series;

[0105] Further, the basic form of the ARIMAX model is:

[0106]

[0107] In the formula, y t is the range division of the current unit time, y t-i is the range division of the lag i order, α is a constant term, is the i-th order autoregressive parameter, p is the order of the autoregressive term, θ j is the j-th order moving average parameter, q is the order of the moving average term, ∈ t-j is the white noise term of the lag j period, ∈ t is the white noise term, representing random error, β k is the coefficient of the exogenous variable X t-k , m is the lag order of the exogenous variable, X t-k is the exogenous variable of the lag k period;

[0108] It should be noted that the exogenous variable part can integrate the influence of other related variables (such as regional attention point quantity difference, regional attention level change value, regional maximum temperature and minimum temperature difference change value, regional attention point strain value average difference and vibration risk coefficient) on range division;

[0109] It should be noted that α, θ j , β kThe acquisition is calculated by a maximum likelihood estimation (MLE) method, and the specific steps are as follows:

[0110] Error term ∈ t Subject to normal distribution N(0,σ 2 ), the likelihood function is:

[0111]

[0112] Taking the logarithm of the likelihood function, the log-likelihood function is obtained:

[0113]

[0114] By maximizing the log-likelihood function, the parameter estimates α, θ j , β k ;

[0115] By the maximum, minimum and average of the prediction result, the prevention area range allocation scheme strategy is dynamically adjusted;

[0116] The application adjusts the attention point adjustment rate result of the prevention area, establishes a time series model for prediction, determines the prevention area range allocation scheme according to the prediction result, reduces the mutual coverage probability of the attention points of each prevention area, improves the monitoring resource utilization efficiency, and reduces the monitoring interference in the area.

[0117] The above formula is a dimensionless value calculation, the formula is obtained by collecting a large amount of data to simulate the formula of the nearest real situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0118] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented 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 processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, 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 or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0119] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0120] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0121] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0122] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. The division of the units is merely logical function division. There can be other division manners in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0123] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0124] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0125] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0126] The above description is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A power communication prevention system based on BeiDou and fiber optic sensing, characterized in that, It includes a data acquisition module, a data processing module, a supplementary evaluation module, and a range division module, with signal connections between each module; The data acquisition module is used to collect characteristic information of Beidou satellites and fiber optic sensors. Through data processing, it obtains the difference in the number of regional points of interest, the change in regional level of interest, the change in the difference between the highest and lowest temperatures in the region, the average difference in strain values ​​of regional points of interest, and the vibration risk coefficient, and sends them to the data processing module. The data processing module is used to obtain the difference in the number of regional concern points, the change in regional concern level, the change in the difference between the highest and lowest temperatures in the region, the average difference in strain values ​​of regional concern points, and the vibration risk coefficient. It establishes a data analysis model to obtain the current monitoring coefficient and the actual risk coefficient of the region, and sends them to the supplementary assessment module. The supplementary assessment module is used to obtain the current monitoring coefficient and the actual risk coefficient of the region, substitute them into fuzzy logic to perform fuzzy reasoning, determine the adjustment ratio of the focus of the prevention area, and send it to the scope division module. The scope division module is used to obtain the attention adjustment ratio results of the prevention area, and to perform scope allocation prediction analysis through time series model, so as to further refine and optimize the scope allocation scheme of the prevention area.

2. The power communication prevention system based on BeiDou and fiber optic sensing according to claim 1, characterized in that, The BeiDou satellite characteristic information includes the difference in the number of regional points of interest and the change in the level of regional interest; the fiber optic sensor characteristic information includes the change in the difference between the highest and lowest temperatures in the region, the average difference in strain values ​​of regional points of interest, and the vibration risk coefficient.

3. A power communication prevention system based on BeiDou and fiber optic sensing according to claim 1 or 2, characterized in that, The differences in the number of regional concern points, the changes in regional concern levels, the changes in the difference between the highest and lowest temperatures in the region, the average differences in strain values ​​of regional concern points, and the vibration risk coefficient are obtained as follows: First, define and count the number of attention points in the current unit of time, analyze the relationship between the number of attention points and the number in the previous unit of time, and then calculate the difference to obtain the difference in the number of attention points in the region. Where i represents the i-th unit time, and g represents the g-th prevention zone; The current regional attention level is calculated by weighting the coverage area and interaction frequency of all attention points within the region and substituting this data into a preset attention level. This result is then subtracted from the regional attention level of the previous unit of time to obtain the change in the regional attention level. By querying the power communication nodes with the highest and lowest temperatures within the region at the current unit of time, and subtracting them, the difference between the highest and lowest temperatures in the region is obtained. This difference is then compared with the difference between the highest and lowest temperatures in the region at the previous unit of time to calculate the change in the difference between the highest and lowest temperatures in the region. The strain values ​​of all points of interest within the region are monitored, and the strain value of each point in the current unit of time and the previous unit of time are recorded. The difference between the two values ​​is calculated to obtain the strain value difference of each point of interest, and then the average difference is calculated to obtain the average strain value difference of the points of interest in the region. By collecting the amplitude and frequency of vibration in the prevention zone in real time, the maximum vibration amplitude and the critical frequency value for equipment loss are determined, and a weighted calculation is performed to obtain the vibration risk coefficient.

4. A power communication prevention system based on BeiDou and fiber optic sensing according to claim 3, characterized in that, Data analysis models were established by acquiring the differences in the number of areas of concern, the changes in the level of concern, the changes in the difference between the highest and lowest temperatures, the average difference in strain values ​​of areas of concern, and the vibration risk coefficient, in order to generate the current monitoring coefficient for the region. and the actual risk coefficient of the region The formula used is: In the formula, This represents the current monitoring coefficient for the region. The actual risk coefficient of the region is ω1, ω2, ω3, ω4 and ω5 are the difference in the number of regional concern points, the change in regional concern level, the change in the difference between the highest and lowest temperatures in the region, the average difference in strain values ​​of regional concern points and the preset proportional coefficient of vibration risk coefficient, respectively, and ω1, ω2, ω3, ω4 and ω5 are all greater than 0.

5. A power communication prevention system based on BeiDou and fiber optic sensing according to claim 1, characterized in that, The current monitoring coefficient and actual risk coefficient of the region are obtained, and then fuzzy logic is used for fuzzy inference to determine the adjustment ratio of the focus points of the prevention area. The specific implementation method is as follows: The current monitoring coefficient and the actual risk coefficient of the region are defined as input variables, and they are divided into different fuzzy sets respectively. The result of the attention adjustment ratio of the prevention area is defined as the output variable and divided into a fuzzy set. Formulate fuzzy rules to describe the impact of the current monitoring coefficient and the actual risk coefficient of the region on the adjustment ratio of the focus of the prevention area; Fuzzy inference is performed based on fuzzy rules to determine the adjustment ratio of the focus of the prevention area.

6. A power communication prevention system based on BeiDou and fiber optic sensing according to claim 1 or 5, characterized in that, The results of the attention adjustment ratio for prevention zones include the attention adjustment ratio for each prevention zone.

7. A power communication prevention system based on BeiDou and fiber optic sensing according to claim 1, characterized in that, The time series model uses the ARIMAX model.

8. A power communication prevention system based on BeiDou and fiber optic sensing according to claim 7, characterized in that, The specific steps for range allocation prediction analysis using time series models are as follows: Step A1: Obtain the data for prediction; Step A2: Create an ARIMAX model; Step A3: Use the maximum likelihood estimation method to estimate the ARIMAX model parameters; Step A4: Verify the effectiveness of the fitted model by checking the goodness of fit of the model using residual analysis. Step A5: Use the fitted model to predict the future allocation scheme, and take the maximum value of the prediction result as the highest point of the range division within the future time period.

9. A power communication prevention system based on BeiDou and fiber optic sensing according to claim 8, characterized in that, The exogenous variables in the ARIMAX model include the difference in the number of regional points of interest, the change in the regional level of concern, the change in the difference between the highest and lowest temperatures in the region, the average difference in strain values ​​at regional points of interest, and the vibration risk coefficient. The basic form of the ARIMAX model is as follows: In the formula, y t To divide the current unit of time into ranges, y t-i For the range of lag order i, α is a constant term. Let θ be the i-th order autoregressive parameter, p be the order of the autoregressive term, and θ be the autoregressive parameter. j Let be the parameter of the j-th order moving average, and q be the order of the moving average term, ∈ t-j It is a white noise term with a lag of j periods, ∈ t It is a white noise term, representing random error, β k It is an exogenous variable X t-k The coefficient, m is the lag order of the exogenous variable, X t-k It is an exogenous variable lagged by k periods.

10. A power communication prevention system based on BeiDou and fiber optic sensing according to claim 9, characterized in that, α、 θ j β k It is obtained by the maximum likelihood estimation method.

Images