Intelligent supervision system for network engineering design and installation

Abstract

The invention discloses an intelligent supervision system for network engineering design and installation, which belongs to the technical field of monitoring and comprises a scene module, a monitoring arrangement module and a monitoring module. The scene module is used for establishing a scene model of a monitoring area, and the scene model is a three-dimensional data model; the monitoring arrangement module is used for carrying out arrangement analysis on each monitoring device and establishing a scene database; identifying a scene model, and matching each initial point from the scene database according to the scene model; performing intrusion analysis adjustment on each initial point to obtain each monitoring point; arranging monitoring equipment according to each monitoring point; the monitoring module is used for monitoring a monitoring area in real time to obtain monitoring data, identifying and analyzing the monitoring data according to a preset intrusion database, judging whether abnormal behaviors exist in the monitoring data or not, and obtaining intrusion identification data; performing corresponding early warning processing according to the intrusion identification data; and generating tracing analysis data according to the intrusion identification data, and inputting the tracing analysis data into the scene initial model.

Description

Technical Field

[0001] The invention belongs to the field of monitoring technology, in particular to a network engineering design and installation intelligent monitoring system. Background Art

[0002] In the current field of monitoring technology, although there are some solutions for monitoring specific areas, these solutions still have some significant shortcomings when dealing with complex and changing monitoring needs.

[0003] First, the existing monitoring system can usually only set monitoring points based on a two-dimensional plane map, which cannot accurately reflect the key information such as the actual terrain, building layout and obstacle distribution of the monitoring area. As a result, the selection of monitoring points often relies on manual experience, lacks scientificity and accuracy, and thus affects the monitoring effect.

[0004] In addition, existing monitoring systems also have certain limitations in anomaly identification. These systems can usually only perform simple matching and screening based on preset anomaly identification features, and cannot dynamically adjust and optimize according to real-time changes in monitoring data. This results in the system being unable to issue early warnings and alarms in a timely and accurate manner when faced with complex and changing abnormal situations, thus affecting the real-time and accuracy of monitoring.

[0005] Based on this, in order to solve the above problems or part of the problems, the present invention provides a network engineering design and installation intelligent supervision system. Summary of the invention

[0006] In order to solve the problems existing in the above-mentioned solutions, the present invention provides an intelligent supervision system for network engineering design and installation.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A network engineering design and installation intelligent supervision system, including a scene module, a monitoring layout module, and a monitoring module;

[0009] The scene module is used to establish a scene model of the monitoring area, and the scene model is a three-dimensional data model.

[0010] Furthermore, the method for establishing the scene model includes:

[0011] Determine a monitoring area and a corresponding monitoring purpose, collect scene data for the monitoring area, and obtain scene data corresponding to the monitoring area; establish an initial scene model according to the scene data, wherein the initial scene model is a three-dimensional data model;

[0012] The corresponding monitoring purpose is marked in the scenario initial model, and a tracing unit is configured for the scenario initial model, wherein the tracing unit is used to generate a tracing record according to the tracing analysis data, and the scenario initial model is marked as a scenario model.

[0013] Furthermore, the traceability unit works as follows:

[0014] Acquire the traceability analysis data, identify the intrusion features corresponding to the traceability analysis data, the intrusion features including the intrusion target, intrusion path, intrusion action, and intrusion time, perform intrusion simulation in the scenario model according to each of the intrusion features, and form a traceability record.

[0015] The monitoring layout module is used to analyze the layout of each monitoring device and establish a scene database, which is used to store monitoring reference data of each scene. The scene monitoring reference data includes a scene model and corresponding locations of each monitoring point;

[0016] Identify the scene model, and match each initial point from the scene database according to the scene model; perform intrusion analysis and adjustment on each initial point to obtain each monitoring point; and arrange monitoring equipment according to each monitoring point.

[0017] Furthermore, the method for establishing the scene database includes:

[0018] Acquire historical monitoring layout data in real time, convert the historical monitoring layout data into scene models and the locations of monitoring points corresponding to the scene models, and integrate them into scene monitoring reference data;

[0019] Establishing a scene database, and inputting each scene monitoring reference data into the scene database for storage;

[0020] The scene monitoring data stored in the scene database are identified in real time, and data expansion is performed in the scene database according to the scene monitoring reference data to obtain expanded scene monitoring reference data.

[0021] Furthermore, the method for expanding data in the scene database according to the monitoring reference data of each scene includes:

[0022] Setting training materials according to the scene models corresponding to the scene monitoring reference data and each monitoring point, and establishing a data expansion model according to the training materials;

[0023] The scene simulation is adjusted according to each scene model in the training material to obtain each scene simulation model, and each scene simulation model is analyzed through the data expansion model to obtain the location of each monitoring point corresponding to each scene simulation model, which is integrated into scene monitoring reference data.

[0024] Furthermore, the method of performing intrusion analysis adjustment on each initial point includes:

[0025] Step SA1: obtaining a scenario model, marking each initial point in the scenario model, and setting corresponding monitoring performance data for each initial point;

[0026] Identify the monitoring purpose, collect in real time each intrusion behavior and the intrusion method corresponding to each intrusion behavior according to the monitoring purpose, count the intrusion-related data corresponding to each intrusion behavior according to the intrusion method corresponding to the intrusion behavior, the intrusion-related data includes the intrusion risk level corresponding to the intrusion behavior under each intrusion method and the share proportion corresponding to each intrusion method under the corresponding intrusion behavior; integrate each intrusion behavior, intrusion method and intrusion-related data to establish an intrusion database;

[0027] Generate an intrusion simulation set according to the intrusion database, wherein the intrusion simulation set is composed of various intrusion simulation data; establish an intrusion simulation evaluation model, wherein the intrusion simulation evaluation model is used to perform intrusion evaluation on various intrusion simulation data;

[0028] Step SA2: performing iterative simulation evaluation on each intrusion simulation data in the intrusion simulation set through the intrusion simulation evaluation model and the scenario model to obtain an intrusion identification value corresponding to each intrusion simulation data;

[0029] Step SA3: evaluating whether the settings of each initial point meet the monitoring requirements according to each intrusion identification value;

[0030] When it is determined that the monitoring requirements are not met, the initial point is adjusted, the scene model is updated, and the process returns to step SA2;

[0031] When it is determined that the monitoring requirements are met, a combination of monitoring points to be selected is obtained; the initial point is adjusted and the scene model is updated;

[0032] Step SA4: Loop step SA2 to step SA3 until the loop termination condition is reached; obtain each candidate monitoring point combination, estimate the monitoring cost of each candidate monitoring point combination, screen each candidate monitoring point combination according to the estimated cost, obtain the target monitoring point combination, and determine each monitoring point according to the target monitoring point combination.

[0033] Further, the method of evaluating whether the setting of each initial point meets the monitoring requirements according to each intrusion identification value includes:

[0034] Obtain intrusion-related data corresponding to each intrusion identification value from the intrusion database, identify the share ratio and intrusion risk level corresponding to each intrusion identification value; integrate the intrusion identification values, share ratios and intrusion risk levels into an intrusion assessment set;

[0035] Obtain monitoring requirements, perform evaluation and analysis based on the monitoring requirements and evaluation sets, and determine whether the settings of each initial point can meet the monitoring requirements.

[0036] Furthermore, the method of performing evaluation analysis according to the monitoring requirements and the evaluation set includes:

[0037] An intelligent evaluation model is established according to monitoring requirements. The expression of the intelligent evaluation model is:

[0038]

[0039] Where: U is the input data, the input data is the evaluation set; the output data is the intelligent evaluation value PR(U);

[0040] The evaluation set is analyzed through the intelligent evaluation model to determine whether the settings of each initial point can meet the monitoring requirements.

[0041] The monitoring module is used to monitor the monitoring area in real time, obtain monitoring data, identify and analyze the monitoring data according to a preset intrusion database, determine whether there is abnormal behavior in the monitoring data, and obtain intrusion identification data; perform corresponding early warning processing according to the intrusion identification data; and generate traceability analysis data according to the intrusion identification data, and input the traceability analysis data into the scene initial model.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] Intelligent supervision of the monitoring area is achieved through the mutual cooperation among the scene module, monitoring layout module and monitoring module; by using the scene database to determine each initial point, it is convenient to improve the analysis efficiency of each initial point, and at the same time, the platform has sufficient time to check and adjust each monitoring point obtained by expanding the data expansion model; if the scene model is analyzed directly through the data expansion model during the application process, it will lead to reduced efficiency and the accuracy will not be as high as that obtained based on the scene database, and it will not be conducive to reducing the data processing volume for subsequent adjustments to each initial point. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] 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.

[0045] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] like Figure 1 As shown, a network engineering design and installation intelligent supervision system includes a scene module, a monitoring layout module, and a monitoring module;

[0048] The scene module is used to establish a scene model of the monitoring area, determine the monitoring purpose corresponding to the monitoring area from the user, and determine it specifically according to the purpose and function of the monitoring area, such as prohibiting outsiders from entering, identifying abnormal personnel, etc., and set it specifically according to its actual needs. For example, the corresponding monitoring purposes of construction areas and office areas are different; collect data from the monitoring area to obtain scene data corresponding to the monitoring area, such as video data, image data, etc. that can accurately represent the actual situation in the scene, and establish a scene initial model based on the scene data. The scene initial model is a three-dimensional data model for intuitively displaying the scene area. Specifically, it is established using existing modeling technology, and it is best to use intelligent algorithms to intelligently generate according to the scene data;

[0049] The corresponding monitoring purpose is marked for the scene initial model, and a tracing unit is configured for the scene initial model, wherein the tracing unit is used to generate a tracing record according to the tracing analysis data, and the user can intuitively understand the corresponding intrusion situation according to the tracing record. The current scene initial model is marked as a scene model.

[0050] In one embodiment, the tracing unit works as follows:

[0051] Obtain traceability analysis data, identify intrusion features such as intrusion targets, intrusion paths, intrusion actions, intrusion time, etc. corresponding to the traceability analysis data, perform intrusion simulation in the scenario model according to each intrusion feature, and form a traceability record.

[0052] The monitoring arrangement module is used to determine each monitoring point and arrange monitoring equipment according to each monitoring point.

[0053] In one embodiment, the monitoring points may be determined based on existing technologies.

[0054] In one embodiment, the method for determining the monitoring point includes:

[0055] Establishing a scene database, the scene database is used to store the monitoring reference data of each scene, the scene monitoring reference data includes the scene model and the corresponding location of each monitoring point;

[0056] Identify the scene model, match each monitoring point from the scene database according to the scene model, and mark them as initial points; by using the scene database to determine each initial point, it is convenient to improve the analysis efficiency of each initial point, and at the same time, the platform also has sufficient time to check and adjust each monitoring point obtained by expanding the data expansion model; if the scene model is analyzed directly through the data expansion model during the application process, it will lead to reduced efficiency and the accuracy will not be as high as that obtained based on the scene database, and it will not be conducive to reducing the data processing volume for subsequent adjustments to each initial point.

[0057] Perform intrusion analysis and adjustment on each initial point to obtain each monitoring point.

[0058] In one embodiment, each initial point may be determined based on prior art.

[0059] In one embodiment, the method for establishing a scene database includes:

[0060] Acquire existing scene models and their corresponding monitoring point locations in real time, and integrate the scene models and the corresponding monitoring point locations into monitoring reference data; this part is generated using existing data, and can also be set in combination with existing historical monitoring layout data, that is, to form corresponding scene models and monitoring point locations based on historical monitoring layout data; it can be summarized as converting historical monitoring layout data into scene models and the monitoring point locations corresponding to each scene model, and integrating them into scene monitoring reference data;

[0061] Establish a scene database, input the monitoring reference data of each scene into the scene database for storage; and identify the monitoring data of each scene stored in the scene database in real time, expand the data in the scene database according to the monitoring reference data of each scene, and obtain the expanded monitoring reference data of each scene. Because the initial point setting of the subsequent application monitoring point does not require extremely high accuracy, data expansion can be performed.

[0062] In one embodiment, a method for expanding data in a scene database according to monitoring reference data of each scene includes:

[0063] According to the scene models corresponding to the reference data of each scene monitoring and the training materials for each monitoring point, a data expansion model is established based on the training materials. The data expansion model is used to adjust the scene models in combination with various possible scenes and monitoring purposes to form different input data. The input data is the scene model. The input data is subsequently analyzed to obtain the position of each monitoring point. Specifically, the data expansion model is established based on various existing intelligent technologies, such as based on CNN network or DNN network, and the training set is set in combination with the training materials for training.

[0064] According to the scene models in the training materials, scene simulation adjustments are performed to obtain each scene simulation model. Each scene simulation model is analyzed through the data expansion model to obtain the location of each monitoring point corresponding to each scene simulation model, and integrated into scene monitoring reference data.

[0065] In one embodiment, the method for performing intrusion analysis adjustment on each initial point includes:

[0066] Step SA1: Obtain a scene model, mark each initial point in the scene model, and set corresponding monitoring performance data for each initial point according to the performance of the monitoring device; and subsequently determine whether the corresponding intrusion behavior can be identified based on each monitoring performance data;

[0067] Identify the purpose of monitoring, and obtain in real time various possible intrusion behaviors and the corresponding intrusion methods of each intrusion behavior in the monitoring area according to the monitoring purpose. Based on big data technology, intelligently screen various intrusion behaviors that have occurred under the corresponding monitoring purpose and scene background. For example, direct intrusion behavior can correspond to strangers ignoring security warnings and directly pushing open or destroying doors, windows and other obstacles to enter the monitoring area; that is, one intrusion behavior may have multiple intrusion methods, which are synchronously counted in combination with the corresponding historical data, and the intrusion risk level of each intrusion behavior under each monitoring purpose and intrusion method, as well as the share of each intrusion method can be counted; the intrusion risk level can be determined in combination with the corresponding loss, generally based on the existing risk assessment standards, or according to the risk standards preset by the user; the above data is integrated into intrusion-related data. Because the monitoring purpose has been determined, the intrusion-related data includes the intrusion risk level corresponding to the intrusion behavior under each intrusion method and the share of each intrusion method under the corresponding intrusion behavior;

[0068] Integrate each intrusion behavior, intrusion method and intrusion-related data to establish an intrusion database; and dynamically update the intrusion database based on subsequent data updates.

[0069] Generate an intrusion simulation set according to the intrusion database. The intrusion simulation set consists of different intrusion simulation data. The intrusion simulation data is generated according to the intrusion mode of the corresponding intrusion behavior, which is used to simulate the intrusion situation of the monitoring area. Specifically, it can be generated in combination with various historical intrusion data;

[0070] Establish an intrusion simulation evaluation model. The intrusion simulation evaluation model is used to perform intrusion evaluation on each intrusion simulation data according to the monitoring performance data and monitoring operation mode of each initial point, and judge whether the intrusion behavior can be identified this time, that is, to perform monitoring operation simulation according to each monitoring performance data and monitoring operation mode, and then perform intrusion simulation from any stage and any possible position in the operation process, and perform intrusion identification according to the monitoring situation of the corresponding operation stage to judge whether the intrusion behavior can be identified; based on the above simulation process, establish an intrusion simulation evaluation model, which can be specifically established based on a neural network such as a CNN network or a DNN network, or can be established based on other intelligent algorithms;

[0071] Step SA2: Perform iterative simulation evaluation on each intrusion simulation data in the intrusion simulation set through the intrusion simulation evaluation model and the scenario model to obtain the intrusion identification value corresponding to each intrusion simulation data. The intrusion identification value is the intrusion behavior identification probability statistically calculated during multiple simulations.

[0072] Step SA3: evaluating whether the settings of each initial point meet the monitoring requirements according to each intrusion identification value;

[0073] When it is determined that the monitoring requirements are not met, the initial point is adjusted and the scene model is updated, that is, the data corresponding to the initial point is adjusted; and step SA2 is returned;

[0074] When it is determined that the monitoring requirements are met, a set of qualified initial point combinations is obtained and marked as candidate monitoring point combinations; the initial points are adjusted and the scene model is updated;

[0075] Step SA4: Loop step SA2 to step SA3 until the preset number of cycles is reached or there is no output of the candidate monitoring point combination, etc., stop the loop, and regard the corresponding preset number of cycles or the fact that there is no output of the candidate monitoring point combination as the loop termination condition; obtain each candidate monitoring point combination, estimate the monitoring cost according to each candidate monitoring point combination, screen each candidate monitoring point combination according to the estimated cost, obtain the target monitoring point combination, and mark each candidate point in the target monitoring point combination as a monitoring point.

[0076] In one embodiment, a method for evaluating whether the settings of each initial point meet the monitoring requirements according to each intrusion identification value includes:

[0077] Obtain intrusion-related data corresponding to each intrusion identification value from the intrusion database, identify the share ratio and intrusion risk level corresponding to each intrusion identification value; integrate the intrusion identification values, share ratios and intrusion risk levels into an intrusion assessment set;

[0078] Preset monitoring requirements according to user requirements, perform evaluation and analysis based on monitoring requirements and evaluation sets, and determine whether the settings of each initial point can meet the monitoring requirements.

[0079] In one embodiment, for evaluation and analysis based on monitoring requirements and evaluation sets, the evaluation set can be used as input data of an existing intelligent model, and an intelligent evaluation model can be established in combination with existing intelligent technologies. Various evaluation sets and corresponding evaluation results can be artificially simulated and set up, and integrated into a training set for training. After successful training, the intelligent evaluation model can be used for evaluation.

[0080] In one embodiment, the evaluation and analysis may be performed based on monitoring requirements and evaluation sets, or may be performed based on other existing technologies.

[0081] In one embodiment, the method for performing evaluation analysis based on monitoring requirements and evaluation sets is:

[0082] An intelligent evaluation model is established according to monitoring requirements. The expression of the intelligent evaluation model is: Where: U is the input data, the input data is the evaluation set; the output data is the intelligent evaluation value PR(U); training is performed by simulating each evaluation set and the evaluation results;

[0083] The evaluation set is analyzed through the intelligent evaluation model to determine whether the settings of each initial point can meet the monitoring requirements.

[0084] In one embodiment, the initial point is adjusted by changing the position of the initial point, increasing or decreasing the initial point, etc., and analysis can be performed based on the intrusion simulation assessment to understand the reasons for not meeting the monitoring requirements, and then the initial point can be adjusted in a targeted manner; specifically, an intelligent model can be established based on existing technologies for intelligent adjustment, such as establishing an intelligent model based on a neural network, and manually establishing a corresponding training set for training, the training set includes input data and output data, the input data is the intrusion simulation assessment data and the initial points, and the output data is the adjusted initial points; intelligent adjustment is performed through the intelligent model after successful training.

[0085] In one embodiment, the initial point is adjusted, and the adjustment may also be performed based on other existing technologies.

[0086] The monitoring module is used to monitor the monitoring area in real time, obtain monitoring data, identify and analyze the monitoring data according to a preset intrusion database, determine whether there is abnormal behavior in the monitoring data, and obtain intrusion identification data, that is, identify according to each intrusion behavior and intrusion method, and determine whether there is abnormal behavior. If there is intrusion behavior, intrusion identification data is generated according to the intrusion behavior, intrusion method, time, location and other data; corresponding early warning processing is performed according to the intrusion identification data; and traceability analysis data is generated according to the intrusion identification data, and the traceability analysis data is input into the scene initial model.

[0087] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0088] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A network engineering design and installation intelligent supervision system, characterized in that: Including scene module, monitoring layout module and monitoring module; The scene module is used to establish a scene model of the monitoring area, and the scene model is a three-dimensional data model; The monitoring layout module is used to analyze the layout of each monitoring device and establish a scene database, which is used to store monitoring reference data of each scene. The scene monitoring reference data includes a scene model and corresponding locations of each monitoring point; Identify the scene model, match each initial point from the scene database according to the scene model; perform intrusion analysis and adjustment on each initial point to obtain each monitoring point; and arrange monitoring equipment according to each monitoring point; The monitoring module is used to monitor the monitoring area in real time, obtain monitoring data, identify and analyze the monitoring data according to a preset intrusion database, determine whether there is abnormal behavior in the monitoring data, and obtain intrusion identification data; Carry out corresponding early warning processing according to intrusion identification data; And generate tracing analysis data according to the intrusion identification data, and input the tracing analysis data into the scene initial model.

2. According to claim 1, a network engineering design and installation intelligent supervision system is characterized in that: The method of establishing the scene model includes: Determine a monitoring area and a corresponding monitoring purpose, collect scene data for the monitoring area, and obtain scene data corresponding to the monitoring area; establish an initial scene model according to the scene data, wherein the initial scene model is a three-dimensional data model; The corresponding monitoring purpose is marked in the scenario initial model, and a tracing unit is configured for the scenario initial model, wherein the tracing unit is used to generate a tracing record according to the tracing analysis data, and the scenario initial model is marked as a scenario model.

3. According to claim 2, a network engineering design and installation intelligent supervision system is characterized in that: The traceability unit works in the following way: Acquire the traceability analysis data, identify the intrusion features corresponding to the traceability analysis data, the intrusion features including the intrusion target, intrusion path, intrusion action, and intrusion time, perform intrusion simulation in the scenario model according to each of the intrusion features, and form a traceability record.

4. According to claim 1, a network engineering design and installation intelligent supervision system is characterized in that: The method for establishing a scene database includes: Acquire historical monitoring layout data in real time, convert the historical monitoring layout data into scene models and the locations of monitoring points corresponding to the scene models, and integrate them into scene monitoring reference data; Establishing a scene database, and inputting each scene monitoring reference data into the scene database for storage; The scene monitoring data stored in the scene database are identified in real time, and data expansion is performed in the scene database according to the scene monitoring reference data to obtain expanded scene monitoring reference data.

5. According to claim 4, a network engineering design and installation intelligent supervision system is characterized in that: The method for expanding data in the scene database according to the monitoring reference data of each scene includes: Setting training materials according to the scene models corresponding to the scene monitoring reference data and each monitoring point, and establishing a data expansion model according to the training materials; The scene simulation is adjusted according to each scene model in the training material to obtain each scene simulation model, and each scene simulation model is analyzed through the data expansion model to obtain the location of each monitoring point corresponding to each scene simulation model, which is integrated into scene monitoring reference data.

6. According to claim 1, a network engineering design and installation intelligent supervision system is characterized in that: Methods for performing intrusion analysis adjustments on each initial point include: Step SA1: obtaining a scenario model, marking each initial point in the scenario model, and setting corresponding monitoring performance data for each initial point; Identify the monitoring purpose, collect in real time each intrusion behavior and the intrusion method corresponding to each intrusion behavior according to the monitoring purpose, count the intrusion-related data corresponding to each intrusion behavior according to the intrusion method corresponding to the intrusion behavior, the intrusion-related data includes the intrusion risk level corresponding to the intrusion behavior under each intrusion method and the share proportion corresponding to each intrusion method under the corresponding intrusion behavior; integrate each intrusion behavior, intrusion method and intrusion-related data to establish an intrusion database; Generate an intrusion simulation set according to the intrusion database, wherein the intrusion simulation set is composed of various intrusion simulation data; establish an intrusion simulation evaluation model, wherein the intrusion simulation evaluation model is used to perform intrusion evaluation on various intrusion simulation data; Step SA2: performing iterative simulation evaluation on each intrusion simulation data in the intrusion simulation set through the intrusion simulation evaluation model and the scenario model to obtain an intrusion identification value corresponding to each intrusion simulation data; Step SA3: evaluating whether the settings of each initial point meet the monitoring requirements according to each intrusion identification value; When it is determined that the monitoring requirements are not met, the initial point is adjusted, the scene model is updated, and the process returns to step SA2; When it is determined that the monitoring requirements are met, a combination of monitoring points to be selected is obtained; the initial point is adjusted and the scene model is updated; Step SA4: Loop step SA2 to step SA3 until the loop termination condition is reached; obtain each candidate monitoring point combination, estimate the monitoring cost of each candidate monitoring point combination, screen each candidate monitoring point combination according to the estimated cost, obtain the target monitoring point combination, and determine each monitoring point according to the target monitoring point combination.

7. A network engineering design and installation intelligent supervision system according to claim 6, characterized in that: Methods for evaluating whether the settings of each initial point meet the monitoring requirements based on each intrusion identification value include: Obtain intrusion-related data corresponding to each intrusion identification value from the intrusion database, identify the share ratio and intrusion risk level corresponding to each intrusion identification value; integrate the intrusion identification values, share ratios and intrusion risk levels into an intrusion assessment set; Obtain monitoring requirements, perform evaluation and analysis based on the monitoring requirements and evaluation sets, and determine whether the settings of each initial point can meet the monitoring requirements.

8. A network engineering design and installation intelligent supervision system according to claim 7, characterized in that: Methods for conducting assessment analysis based on monitoring requirements and assessment sets include: An intelligent evaluation model is established according to monitoring requirements. The expression of the intelligent evaluation model is: Where: U is the input data, the input data is the evaluation set; the output data is the intelligent evaluation value PR(U); The evaluation set is analyzed through the intelligent evaluation model to determine whether the settings of each initial point can meet the monitoring requirements.

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