An intelligent monitoring method for fuze controller

Abstract

The invention discloses an intelligent monitoring method for a fuze controller, which belongs to the technical field of fuze controller monitoring. The method comprises: analyzing a monitoring target to determine external interference data corresponding to the monitoring target; determining an application area of ​​the monitoring target, collecting data for the application area according to a preset external collection template, and obtaining external influence data; analyzing the external influence data to determine an external monitoring target corresponding to the monitoring target in the application area; performing interference evaluation on the application area according to each external monitoring target, and obtaining a corresponding regional interference analysis diagram; displaying the regional interference analysis diagram to a user, and performing corresponding anti-interference processing; acquiring corresponding monitoring data in real time, performing interference evaluation on the monitoring data, and obtaining corresponding interference evaluation results; and performing corresponding processing according to the interference evaluation results.

Description

Technical Field

[0001] The invention belongs to the technical field of fuze controller monitoring, and in particular is an intelligent monitoring method for a fuze controller. Background Art

[0002] In actual applications, the fuze controller is often interfered and affected by various internal and external factors, causing it to fail to work properly or its performance to degrade. For example, in a complex electromagnetic environment, the fuze controller may be interfered with by external electromagnetic sources, such as radar, radio communication, etc. These interferences may interfere with the signal transmission of the fuze controller and even cause malfunctions. When the fuze controller works in a high or low temperature environment, the performance of its internal electronic components may change, thus affecting the normal operation of the fuze controller. For example, high temperature may accelerate the aging of components, while low temperature may cause the performance of components to degrade.

[0003] In order to solve the above problems, the present invention provides an intelligent monitoring method for a fuze controller. Summary of the invention

[0004] In order to solve the problems existing in the above-mentioned solutions, the present invention provides an intelligent monitoring method for a fuze controller.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for intelligent monitoring of a fuze controller, the method comprising:

[0007] Step 1: Analyze the monitoring target to determine the external interference data corresponding to the monitoring target, wherein the external interference data includes first interference data and second interference data;

[0008] Furthermore, the method for determining the external interference data includes:

[0009] Collect material analysis data according to monitoring objectives. Material analysis data includes similar material data and technical material data;

[0010] Analyze the same type of material data to determine each first interference data;

[0011] Performing simulation analysis on the technical material data to determine each second interference data;

[0012] The first interference data and the second interference data are integrated into external interference data of the monitoring target.

[0013] Furthermore, the method for analyzing the same type of material data includes:

[0014] Identify the control abnormality causes corresponding to each similar material data, eliminate the control abnormality causes that are not caused by external interference, and mark the remaining control abnormality causes as initial similar causes; calculate the corresponding background occurrence rate and abnormality rate based on the initial similar causes;

[0015] According to the formula Calculate the corresponding similarity judgment value;

[0016] Where: PA is the same type judgment value; μ is the active value; GL is the background value; YL is the abnormal probability; e is the natural constant;

[0017] Mark the initial similar cause whose similar judgment value is greater than the threshold value X1 as the first similar cause;

[0018] Acquire condition data corresponding to the first similar cause, and label the condition data with corresponding first similar cause labels; and integrate each condition data into first interference data.

[0019] Furthermore, the method for simulating and analyzing the technical material data includes:

[0020] Generate corresponding simulation conditions according to technical material data, establish an application judgment model, analyze each simulation condition through the application judgment model, and obtain the application judgment value corresponding to each simulation condition;

[0021] Establish a material simulation model; simulate the simulation conditions according to the material simulation model to obtain a corresponding simulation curve;

[0022] Fit the simulation curve to obtain the simulation function, which is marked as H(t), where t is time; according to the formula Calculate the corresponding technical judgment value;

[0023] Where: PB is the technical judgment value; PD(s) is the application judgment value;

[0024] The simulation conditions with technical judgment values ​​greater than the threshold value X2 are marked as initial conditions;

[0025] The corresponding second target condition data are set according to the initial conditions; the corresponding initial condition labels are added to the second target condition data; and each second target condition data is integrated into the second interference data.

[0026] Furthermore, the expression of the application judgment model is:

[0027] ;

[0028] Where: s is the input data; the output data is the application judgment value 1 or 0.

[0029] Step 2: Determine the application area of ​​the monitoring target, collect data in the application area according to the preset external collection template, and obtain external impact data; analyze the external impact data to determine the external monitoring target corresponding to the monitoring target in the application area;

[0030] Furthermore, the method for analyzing the external influence data includes:

[0031] Obtaining external interference data of the monitoring target, determining each candidate monitoring target according to the external interference data, performing feature judgment on the external influence data according to each candidate monitoring target, and obtaining corresponding feature judgment results;

[0032] When the feature judgment result shows that the candidate monitoring target meets the feature judgment requirements, the corresponding candidate monitoring target is marked as an external monitoring target;

[0033] When the feature judgment result shows that the candidate monitoring target does not meet the feature judgment requirements, the corresponding candidate monitoring target will be eliminated.

[0034] Step 3: Conduct interference assessment on the application area according to each external monitoring target and obtain the corresponding regional interference analysis diagram;

[0035] Furthermore, the method of performing interference assessment on the application area according to each external monitoring target includes:

[0036] Obtain the target usage corresponding to the monitoring target, obtain the external impact data of the application area and the external interference data corresponding to each external monitoring target;

[0037] Set up an initial regional map based on external impact data and external monitoring targets, and mark the interference areas corresponding to each external monitoring target on the initial regional map;

[0038] Initially merge each position in the initial map of the region to obtain each unit area, conduct interference assessment on each unit area, and obtain the corresponding interference risk value;

[0039] Calculate the absolute value of the difference in interference risk values ​​between adjacent unit areas, and mark it as the evaluation difference;

[0040] When the evaluation difference is less than the threshold value X3, the corresponding unit areas are merged to obtain a merged area, and the interference risk value corresponding to the merged area is calculated;

[0041] When the evaluation difference is not less than the threshold value X3, no merging is performed;

[0042] And so on, until there are no adjacent merged areas that meet the merge requirements, the remaining unit areas or merged areas are marked as interference risk areas; the interference risk areas are marked accordingly in the regional initial map, and the marked regional initial map is marked as the regional interference analysis map.

[0043] Further, the method for interfering evaluation of each unit area includes:

[0044] Identifying the external influence data and external monitoring targets corresponding to each unit area, and obtaining the external interference data corresponding to the external monitoring targets; evaluating the external influence data and the external interference data according to the target use to obtain the corresponding single interference value;

[0045] Marking the obtained single interference value as DCi, where i represents the corresponding external monitoring target, i = 1, 2,..., n, and n is a positive integer;

[0046] According to the formula Calculating the corresponding interference risk value;

[0047] In the formula: GO is the interference risk value, i is the proportionality coefficient corresponding to the corresponding external monitoring target, and the value range is 0 < bi ≤ 1; e is the natural constant.

[0048] Further, the method for calculating the interference risk value of the merged area includes:

[0049] Identifying each unit area corresponding to the merged area, obtaining the unit area and the interference risk value corresponding to each unit area, and marking the obtained unit area and the interference risk value as Aj and GOj respectively, where j represents the corresponding unit area, j = 1, 2,..., m, and m is a positive integer;

[0050] According to the formula Calculating the interference risk value corresponding to the merged area;

[0051] In the formula: GO' is the interference risk value corresponding to the merged area.

[0052] Step Four: Display the area interference analysis diagram to the user and perform corresponding anti-interference processing;

[0053] Step Five: Real-time obtain the corresponding monitoring data, perform interference evaluation on the monitoring data to obtain the corresponding interference evaluation result; perform corresponding processing according to the interference evaluation result.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] By being able to real-time monitor the status and environmental parameters of the fuse controller, such as temperature, humidity, electromagnetic field strength, etc. Once an abnormal situation is detected, the system can immediately issue an alarm and take corresponding control measures to ensure the safe and reliable operation of the fuse controller. In addition, by collecting and analyzing the operation data of the fuse controller, users can also discover potential problems and hidden dangers so as to make timely improvements and maintenance. Description of the Drawings

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0058] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] like Figure 1 As shown, a method for intelligent monitoring of a fuze controller includes:

[0060] Step 1: Obtain the fuze controller that needs to be monitored and mark it as the monitoring target; collect material analysis data according to the monitoring target, and the material analysis data includes similar material data and technical material data; that is, collect data according to the model, working principle, components, etc. of the monitoring target, collect historical data of the same type of fuze controller that causes control abnormality due to external interference factors, and regard it as similar material data; conduct technical retrieval according to the working principle, components, etc., retrieve the technical data that may have an interference effect on it and cause its control abnormality, and regard it as technical material data; specifically, perform retrieval based on existing retrieval technology;

[0061] Analyze the same type of material data to determine each first interference data;

[0062] Performing simulation analysis on the technical material data to determine each second interference data;

[0063] The first interference data and the second interference data are integrated into external interference data of the monitoring target.

[0064] Among them, the method of analyzing the data of the same type of materials includes:

[0065] Identify the control abnormality causes corresponding to each similar material data, eliminate the control abnormality causes that are not caused by external interference, and mark the remaining control abnormality causes as initial similar causes; calculate the corresponding background occurrence rate and abnormality rate based on the similar material data corresponding to the initial similar causes, where the background occurrence rate is calculated based on the overall similar material data to determine the probability of the application background corresponding to the control abnormality caused by the initial similar cause; the abnormality rate is the probability of the control abnormality occurring when the application background appears;

[0066] According to the formula Calculate the corresponding similarity judgment value;

[0067] Wherein: PA is the same category judgment value; μ is the active value, the active value is based on whether the initial same category cause is human active interference, and non-active interference corresponds to a value. When it is active interference, the application scenario of the monitoring target is determined, and the loss caused by the active interference in the impact scenario is analyzed, and then a value greater than the non-active interference is set; the corresponding value is the active value; the application scenario of the monitoring target is what device the monitoring target is applied on, and in what scenario the device will be applied; specifically, the existing settings can be used, such as establishing a corresponding active value analysis model based on a neural network such as a DNN network, and manually establishing a corresponding training set for training. The training set includes input data and output data. The input data is the initial same category cause and the application data of the monitoring target, and the output data is the active value; GL is the background occurrence rate; YL is the abnormality rate; e is a natural constant.

[0068] The initial similar cause whose similar judgment value is greater than the threshold value X1 is marked as the first similar cause, and the conditional data that causes the monitoring and control abnormality according to the first similar cause is obtained. The conditional data is the circumstances under which the monitoring target control abnormality is caused; the conditional data is marked with the corresponding first similar cause label; and each conditional data is integrated into the first interference data.

[0069] Methods for simulating and analyzing technical material data include:

[0070] Establish a material simulation model of the monitoring target, that is, an intelligent model that can simulate the actual situation based on the physical and chemical properties of the monitoring target and existing technologies, such as digital twin technology;

[0071] Generate each simulation condition according to the technical material data, that is, determine what kind of active interference method will be used for interference according to the technical material data, and then directly apply the simulation according to the interference principle described, which is equivalent to assuming that the interference principle can be realized and directly interfere; evaluate the application of each simulation condition according to the application scenario of the monitoring target to determine whether the simulation condition meets the interference application standard. The interference application standard is whether it is worth or possible for someone to use this active method to interfere. Specifically, the corresponding interference application standard is determined according to user needs, and the possible ways to achieve the simulation condition based on the existing technology are evaluated according to the interference application standard. As long as there is a possible way that meets the interference application standard, it is judged that it meets the interference application standard; establish the corresponding application judgment model based on the above principle, and the expression of the application judgment model is: ; Where: s is the input data, namely technical material data, simulation conditions and application scenarios; the output data is the application judgment value 1 or 0; the corresponding application judgment value is obtained by analyzing the application judgment model.

[0072] The simulation conditions are simulated according to the material simulation model to obtain the corresponding simulation curve. The horizontal axis of the simulation curve is time, and the vertical axis is the interference impact rate. The interference impact rate is judged according to the degree of its influence on the functions of the monitoring target. The value range is [0, 100]. When the monitoring target fuze control function cannot be exerted, the interference impact value is 100; the interference impact value of no influence is 0, and the corresponding interference impact value is set according to the difference in influence. The specific adjustment is based on the user's needs for its functions. Generally, the platform will set the corresponding interference impact value matching table through professionals, and the user will adjust the interference impact value matching table according to needs;

[0073] Fit the simulation curve to obtain the corresponding simulation function, which is marked as H(t), where t is time; according to the formula Calculate the corresponding technical judgment value;

[0074] Where: PB is the technical judgment value; PD(s) is the application judgment value;

[0075] The simulation conditions with technical judgment values ​​greater than the threshold value X2 are marked as initial conditions;

[0076] Obtain the implementation method corresponding to the initial condition, and set the corresponding second target condition data according to the implementation method. The second target condition data is similar to the condition data in the analysis of the same type of material data, and is used for subsequent analysis to determine whether the application environment of the monitoring target is the same as the second target condition data, that is, the feature range corresponding to the implementation method;

[0077] The second target condition data are labeled with corresponding initial condition labels; and each second target condition data is integrated into second interference data.

[0078] Step 2: Determine the application area of ​​the monitoring target. The application area is the area where it needs to work, such as the passing area during the movement, the fixed surrounding area, etc.; determine the external data that needs to be collected according to the external interference data corresponding to the monitoring target, set the corresponding external data collection template, and collect data for the application area according to the preset external collection template to obtain external impact data; analyze the external impact data to determine the external monitoring target corresponding to the monitoring target in the application area.

[0079] Methods for analyzing external influence data include:

[0080] Obtain external interference data of the monitoring target, determine each candidate monitoring target according to the external interference data, that is, one first interference data or one second interference data corresponds to one candidate monitoring target; perform feature judgment on the external influence data according to each candidate monitoring target, that is, determine whether the external influence data may have feature data that meets the requirements of the candidate monitoring target, and the corresponding judgment can be achieved by using existing technology. For example, a corresponding intelligent model is established based on a DNN network or a CNN network, and a corresponding training set is established manually for training. The training set includes input data and output data, and the input data is the external influence data and the external interference data corresponding to the candidate monitoring target; the output data is the feature judgment result, that is, whether the candidate monitoring target meets the feature judgment requirements; the corresponding feature judgment result is obtained by analyzing the intelligent model after successful training;

[0081] When the feature judgment result shows that the candidate monitoring target meets the feature judgment requirements, the corresponding candidate monitoring target is marked as an external monitoring target;

[0082] When the feature judgment result shows that the candidate monitoring target does not meet the feature judgment requirements, the corresponding candidate monitoring target will be eliminated.

[0083] Step 3: Conduct interference assessment on the application area according to each external monitoring target and obtain the corresponding regional interference analysis diagram;

[0084] Methods for evaluating interference in the application area based on various external monitoring targets include:

[0085] Obtain the purpose of the carrier corresponding to the monitoring target, mark it as the target purpose, obtain the external impact data of the application area and the external interference data corresponding to each external monitoring target;

[0086] According to the external impact data and the external monitoring target, the regional initial map is set. The regional initial map is a drawing of the application area, but the external impact data of each area position is marked in it. According to the external impact data corresponding to the external monitoring target, the area that may correspond to the external monitoring target is determined and marked as the interference area;

[0087] Initially merge each position in the initial map of the region to obtain each unit area, that is, merge the positions with the same corresponding conditions of adjacent positions, such as the same type, quantity, environmental conditions, etc. of the corresponding external monitoring targets;

[0088] Conduct interference assessment on each unit area to obtain the corresponding interference risk value;

[0089] Calculate the absolute value of the difference in interference risk values ​​between adjacent unit areas, and mark it as the evaluation difference;

[0090] When the evaluation difference is less than the threshold value X3, the corresponding unit areas are merged to obtain a merged area, and the interference risk value corresponding to the merged area is calculated;

[0091] When the evaluation difference is not less than the threshold value X3, no merging is performed;

[0092] And so on, until there are no adjacent merged areas that meet the merge requirements, the remaining unit areas or merged areas are marked as interference risk areas; the interference risk areas are marked accordingly in the regional initial map, and the marked regional initial map is marked as the regional interference analysis map.

[0093] The method for calculating the interference risk value of the merged area includes:

[0094] Identify each unit area corresponding to the merged area, obtain the unit area and interference risk value corresponding to each unit area, and mark the obtained unit area and interference risk value as Aj and GOj respectively, where j represents the corresponding unit area, j=1, 2, ..., m, and m is a positive integer;

[0095] According to the formula Calculate the interference risk value corresponding to the merged area;

[0096] Where: GO' is the interference risk value corresponding to the merged area.

[0097] Methods for interference assessment of each unit area include:

[0098] Identify the external influence data and external monitoring targets corresponding to each unit area, and obtain the external interference data corresponding to the external monitoring targets; evaluate the external influence data and external interference data according to the target use to obtain the probability of interference on the monitoring target, which is marked as a single interference value; specifically, establish a corresponding interference analysis model based on the CNN network or DNN network, and establish a corresponding training set through artificial means for training. The training set includes input data and output data. The input data is the target use, external influence data, and external interference data; the output data is the single interference value; the training set can be set in combination with the records of various existing fuse controllers being interfered; it can also directly preset the single interference value in different situations based on the interference records, and then perform matching.

[0099] Mark the obtained single interference value as DCi, where i represents the corresponding external monitoring target, i = 1, 2, …, n, n is a positive integer, and the value not included in this position is 0;

[0100] According to the formula Calculate the corresponding interference risk value;

[0101] In the formula: GO is the interference risk value, i is the proportionality coefficient corresponding to the corresponding external monitoring target, and the value range is 0 < bi ≤ 1; e is the natural constant.

[0102] Step Four: Display the regional interference analysis diagram to the user for corresponding anti-interference processing according to the displayed interference risk area to reduce the interference risk. Specifically, perform anti-interference processing according to the user's needs and capabilities;

[0103] Step Five: During the application process of the monitoring target, obtain the corresponding monitoring data in real time. The monitoring data is data related to the interference impact on the monitoring target, and can be monitored according to the external interference data. Furthermore, the monitoring data can be evaluated to analyze whether it will be interfered; perform interference evaluation on the monitoring data to obtain the corresponding interference evaluation result; perform corresponding processing according to the interference evaluation result; if there is no interference, no corresponding operation is performed, and if there is interference, corresponding warning processing is performed.

[0104] The above formulas are all calculated by removing the dimension and taking their numerical values. The formula is obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through a large amount of data simulation.

[0105] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for intelligent monitoring of a fuze controller, characterized in that: Methods include: Step 1: Analyze the monitoring target to determine the external interference data corresponding to the monitoring target, wherein the external interference data includes first interference data and second interference data; Step 2: Determine the application area of ​​the monitoring target, collect data in the application area according to the preset external collection template, and obtain external impact data; analyze the external impact data to determine the external monitoring target corresponding to the monitoring target in the application area; Step 3: Conduct interference assessment on the application area according to each external monitoring target and obtain the corresponding regional interference analysis diagram; Step 4: Display the regional interference analysis diagram to the user and perform corresponding anti-interference processing; Step 5: Acquire corresponding monitoring data in real time, perform interference assessment on the monitoring data, and obtain corresponding interference assessment results; Take appropriate measures based on the interference assessment results; Methods for determining external interference data include: Collect material analysis data according to monitoring objectives, including similar material data and technical material data; Analyze the same type of material data to determine each first interference data; Performing simulation analysis on the technical material data to determine each second interference data; Integrate the first interference data and the second interference data into external interference data of the monitoring target; Methods for analyzing external influence data include: Obtaining external interference data of the monitoring target, wherein one first interference data or one second interference data corresponds to one candidate monitoring target, determining each candidate monitoring target according to the external interference data, performing feature judgment on the external influence data according to each candidate monitoring target, and obtaining a corresponding feature judgment result; When the feature judgment result shows that the candidate monitoring target meets the feature judgment requirements, the corresponding candidate monitoring target is marked as an external monitoring target; When the feature judgment result shows that the candidate monitoring target does not meet the feature judgment requirements, the corresponding candidate monitoring target will be eliminated.

2. The intelligent monitoring method for fuze controller according to claim 1, characterized in that: Methods for analyzing similar material data include: Identify the control abnormality causes corresponding to each similar material data, eliminate the control abnormality causes that are not caused by external interference, and mark the remaining control abnormality causes as initial similar causes; calculate the corresponding background occurrence rate and abnormality rate based on the initial similar causes; According to the formula Calculate the corresponding similarity judgment value; Where: PA is the same type judgment value; μ is the active value; GL is the background value; YL is the abnormal probability; e is the natural constant; Mark the initial similar cause whose similar judgment value is greater than the threshold value X1 as the first similar cause; Acquire condition data corresponding to the first similar cause, and label the condition data with corresponding first similar cause labels; and integrate each condition data into first interference data.

3. The intelligent monitoring method for fuze controller according to claim 1 is characterized in that: Methods for simulating and analyzing technical material data include: Generate corresponding simulation conditions according to technical material data, establish an application judgment model, analyze each simulation condition through the application judgment model, and obtain the application judgment value corresponding to each simulation condition; Establish a material simulation model; simulate the simulation conditions according to the material simulation model to obtain a corresponding simulation curve; Fit the simulation curve to obtain the simulation function, which is marked as H(t), where t is time; according to the formula Calculate the corresponding technical judgment value; Where: PB is the technical judgment value; PD(s) is the application judgment value; The simulation conditions with technical judgment values ​​greater than the threshold value X2 are marked as initial conditions; Set the corresponding second target condition data according to the initial conditions; label the second target condition data with the corresponding initial condition labels; integrate each second target condition data into second interference data.

4. The intelligent monitoring method for fuze controller according to claim 3 is characterized in that: The expression for applying the judgment model is: ； In the formula: s is the input data; the output data is the applied judgment value 1 or 0.

5. The intelligent monitoring method for fuze controller according to claim 1, characterized in that: The method for interfering with the application area according to each external monitoring target includes: Obtain the target use corresponding to the monitoring target, obtain the external influence data of the application area and the external interference data corresponding to each external monitoring target; Set an initial area map according to the external influence data and the external monitoring target, and mark the interference areas corresponding to each external monitoring target on the initial area map; Perform initial merging on each position in the initial area map to obtain each unit area, and perform interference evaluation on each unit area to obtain the corresponding interference risk value; Calculate the absolute value of the difference in interference risk values between adjacent unit areas, and label it as the evaluation difference; When the evaluation difference is less than the threshold X3, merge the corresponding unit areas to obtain a merged area, and calculate the interference risk value corresponding to the merged area; When the evaluation difference is not less than the threshold X3, no merging is performed; And so on, until there are no adjacent merged areas that meet the merging requirements, mark the remaining unit areas or merged areas as interference risk areas; mark the interference risk areas on the initial area map correspondingly, and mark the marked initial area map as the area interference analysis map.

6. The intelligent monitoring method for fuze controller according to claim 5, characterized in that: The method for interfering with each unit area includes: Identify the external influence data and external monitoring target corresponding to each unit area, and obtain the external interference data corresponding to the external monitoring target; evaluate the external influence data and external interference data according to the target use to obtain the corresponding single interference value; Label the obtained single interference value as DCi, where i represents the corresponding external monitoring target, i = 1, 2,..., n, and n is a positive integer; According to the formula Calculate the corresponding interference risk value; In the formula: GO is the interference risk value, bi is the proportionality coefficient corresponding to the corresponding external monitoring target, and the value range is 0 < bi ≤ 1; e is the natural constant.

7. The intelligent monitoring method for fuze controller according to claim 5 is characterized in that: The method for calculating the interference risk value of the merged area includes: Identify each unit area corresponding to the merged area, obtain the unit area and interference risk value corresponding to each unit area, and label the obtained unit area and interference risk value as Aj and GOj respectively, where j represents the corresponding unit area, j = 1, 2,..., m, and m is a positive integer; According to the formula Calculate the interference risk value corresponding to the merged area; In the formula: GO' is the interference risk value corresponding to the merged area.

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