A spatial area intelligent monitoring safety warning system for digital blocks

Abstract

The present invention discloses a spatial area intelligent monitoring safety early warning system for digital blocks, which specifically relates to the field of intelligent monitoring technology, including a data acquisition module, an abnormal evaluation module, a comprehensive analysis module, and a risk early warning module. The system evaluates different monitoring devices in the block by acquiring historical maintenance information of the monitoring devices, feedback abnormal image information, and working environment information, screens out monitoring devices that may have abnormal hidden dangers, continuously tracks the status of the monitoring devices, establishes an abnormal hidden danger index set, and generates a risk early warning index by acquiring abnormal hidden danger index outlier information and monitoring strategy switching time information. The risk early warning index is compared with a risk early warning index reference threshold to generate an early warning signal, thereby avoiding serious lag in the next timed and real-time monitoring strategy switching, ensuring that the system can timely analyze the monitoring data of the monitoring area, and can obtain a timely early warning when an abnormal situation occurs in this area.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and more specifically, to a spatial area intelligent monitoring safety early warning system for digital blocks. Background Art

[0002] A spatial area intelligent monitoring security warning system for digital blocks refers to a system that installs various sensors and other intelligent monitoring equipment at multiple angles in the block to capture audio-visual data in the area and send the collected data to the central processing unit. The system analyzes camera images and sensor data through machine learning or artificial intelligence technology, and can issue early warning prompts when abnormal situations such as criminal behavior, fire, traffic accidents, etc. are detected.

[0003] The existing technology has the following deficiencies: when monitoring the area of ​​the block through sensors and monitoring equipment, some managers will adopt a combination of timed and real-time dynamic monitoring according to the actual situation of the block in order to save costs. However, when there are potential abnormalities in the operating status of some sensors or monitoring equipment, it is difficult for managers to discover the abnormalities formed in the early stage. As time goes by, it is not discovered until there is an obvious fault in the operating status. This may cause serious lags in the switching of timed and real-time monitoring strategies. The system cannot analyze the monitoring data of the corresponding area in time, and when abnormal conditions occur in this area, timely warnings cannot be obtained.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a spatial area intelligent monitoring safety warning system for a digital block to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A spatial area intelligent monitoring safety warning system for digital blocks, including a data acquisition module, an abnormality assessment module, a comprehensive analysis module, and a risk warning module, and each module is connected by a signal;

[0008] The data acquisition module is used to collect the historical maintenance information of the monitoring equipment, the feedback abnormal image information and the working environment information. After the collection, the historical maintenance information, the abnormal image information and the working environment information are transmitted to the abnormal assessment module;

[0009] The abnormality assessment module is used to normalize the historical maintenance information, abnormal image information and working environment information, establish an abnormality assessment model, generate an abnormal hidden danger index, and compare the abnormal hidden danger index with the abnormal hidden danger index reference threshold, evaluate the monitoring equipment at different angles of the block, and screen the monitoring equipment in abnormal conditions;

[0010] A comprehensive analysis module is used to establish an abnormal hidden danger index analysis set based on the abnormal hidden danger index subsequently generated by the abnormal monitoring equipment, and to establish a risk warning model and generate a risk warning index by obtaining the abnormal hidden danger index outlier information and monitoring strategy switching time information;

[0011] The risk warning module is used to compare the risk warning index with the risk warning index reference threshold and select whether to generate a warning signal.

[0012] In a preferred embodiment, the data acquisition module is used to collect historical maintenance information, feedback abnormal image information and working environment information of the monitoring equipment;

[0013] The historical maintenance information includes the abnormal maintenance frequency coefficient, the abnormal image information includes the abnormal fuzzy measurement coefficient, and the working environment information includes the electromagnetic interference abnormal fluctuation coefficient. After collection, the data acquisition module calibrates the abnormal maintenance frequency coefficient, the abnormal fuzzy measurement coefficient, and the electromagnetic interference abnormal fluctuation coefficient as GA, AT, and OJ, respectively.

[0014] In a preferred embodiment, the logic for obtaining the abnormal maintenance frequency coefficient is as follows:

[0015] Set the baseline threshold PL for monitoring equipment maintenance frequency z ;

[0016] Get the maintenance frequency within time T and mark the maintenance frequency as PL. When PL>PL z When PL>PL z The maintenance frequency is marked as PL α ;

[0017] Calculate the abnormal maintenance frequency coefficient, the expression is as follows: Wherein, i represents the order number of the abnormal maintenance frequency, i={1, 2, . . . , n}, and n is a positive integer.

[0018] In a preferred embodiment, the logic for obtaining the abnormal fuzzy measurement coefficient is as follows:

[0019] Obtain the video data fed back by the monitoring device within T time, decompose the obtained video data to obtain a series of image frames, and number the decomposed image frames to obtain an image frame set OR = {QP j}={QP1,QP2,...,QPm}, where j represents the sequence number of each image frame, j = {1, 2, ..., m}, m is a positive integer;

[0020] Set the baseline threshold of the fuzzy measure LM and mark it as LM z ;

[0021] The blur measure of each image frame in the image frame set is compared with the blur measure reference threshold. When LM>LM z When LM>LM z The fuzzy measure is denoted as LM β ;

[0022] Calculate the abnormal fuzzy measure coefficient, and its expression is as follows: in, Indicates that the kth LM>LM z Blur measure of the image frame, PO means LM>LM z The average value of the fuzzy measure, k means LM>LM z The number of blur measurement image frames, k = {1, 2, ..., h}, h is a positive integer.

[0023] In a preferred embodiment, obtaining the image frame blur measure LM comprises the following steps:

[0024] Image preprocessing: performing preprocessing operations on images in an image frame set;

[0025] Fourier transform: Convert an image from the spatial domain (pixel domain) to the frequency domain using Fourier transform;

[0026] Spectral analysis: In the frequency domain, the spectral components of the image are analyzed to obtain high-frequency and low-frequency information;

[0027] Calculate the blur measure: According to the results of frequency domain analysis, the blur measure is used to quantify the blur degree of the image. The expression is as follows: LM = ∑ [(YS (v,u) - QP j (v,u))^2 / (M*N)], where M and N represent the width and height of the image, respectively, YS(v,u) and QP j (v,u) represent the preset standard image YS and the image frame QP in the image frame set respectively j The pixel value at position (v,u).

[0028] In a preferred embodiment, the logic for obtaining the electromagnetic interference abnormal fluctuation coefficient is as follows:

[0029] Set the baseline threshold range of electromagnetic intensity that the monitoring equipment can withstand when working, and mark it as DB z min~DB z max , where DB z min Indicates the minimum value of the electromagnetic intensity that the monitoring equipment can withstand when working, DB z max Indicates the maximum value of the electromagnetic intensity that the monitoring equipment can withstand when working;

[0030] An electromagnetic field detector is installed in the block scene where the monitoring device is located to obtain the intensity of the electromagnetic field within the time T in real time, and mark it as DB;

[0031] Calculate the abnormal fluctuation coefficient of electromagnetic interference, the expression is as follows: Wherein, x represents the number of times the electromagnetic intensity in the block scene where the monitoring device is located exceeds the reference threshold range, x={1, 2, ..., y}, y is a positive integer, [θ1,θ2] is the start time and end time of each time the electromagnetic intensity exceeds the reference threshold range.

[0032] In a preferred embodiment, the abnormal assessment module normalizes the acquired abnormal maintenance frequency coefficient GA, abnormal fuzzy measurement coefficient AT, and electromagnetic interference abnormal fluctuation coefficient OJ, and establishes an abnormal assessment model to generate an abnormal hidden danger index and mark it as YH, which is expressed as follows: Wherein, a1, a2, and a3 are preset proportional coefficients of abnormal maintenance frequency coefficient GA, abnormal fuzzy measurement coefficient AT, and electromagnetic interference abnormal fluctuation coefficient OJ, respectively, and a1>a2>a3>0;

[0033] Compare the abnormal hidden danger index with the abnormal hidden danger index reference threshold to screen out abnormal monitoring equipment;

[0034] If the abnormal hidden danger index is greater than or equal to the abnormal hidden danger index reference threshold, the monitoring device is marked as an abnormal operation monitoring device.

[0035] In a preferred embodiment, the comprehensive analysis module establishes an abnormal hidden danger index analysis set for the abnormal hidden danger index subsequently generated by the abnormal monitoring equipment, and establishes a risk warning model by obtaining the abnormal hidden danger index outlier information and the monitoring strategy switching time information to generate a risk warning index.

[0036] In a preferred embodiment, the risk warning module compares the risk warning index with a risk warning index reference threshold;

[0037] If the risk warning index is greater than or equal to the risk warning index reference threshold, a warning signal is generated;

[0038] If the risk warning index is less than the risk warning index reference threshold, there is no need to generate a warning signal.

[0039] Technical effects and advantages of the present invention:

[0040] 1. The present invention evaluates different monitoring devices in the block by acquiring historical maintenance information, feedback abnormal image information and working environment information of monitoring devices, screens out monitoring devices that may have abnormal hidden dangers, continuously tracks the status of monitoring devices, establishes an abnormal hidden danger index set, and obtains YH in the abnormal hidden danger index set. s Abnormal hidden danger outlier degree value, YH s The difference between the evaluation time point and the monitoring strategy switching time point is used to generate a risk warning index. The risk warning index is compared with the risk warning index reference threshold, and a warning is issued at a reasonable time point to prompt management personnel to make corresponding processing to avoid serious lags in the next scheduled and real-time monitoring strategy switching, and to ensure that the system can analyze the monitoring data of the monitored area in a timely manner, so that when an abnormal situation occurs in this area, a timely warning can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0042] Figure 1 This is a schematic diagram of the structure of Example 1 of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0044] Example 1

[0045] The present invention provides Figure 1 The spatial area intelligent monitoring safety warning system for a digital block shown in the figure includes a data acquisition module, an abnormality assessment module, a comprehensive analysis module, and a risk warning module, and each module is connected by a signal;

[0046] The data acquisition module collects the historical maintenance information of the monitoring equipment, the feedback abnormal image information and the working environment information, and after the collection, transmits the historical maintenance information, the abnormal image information and the working environment information to the abnormal assessment module;

[0047] The historical maintenance information includes the abnormal maintenance frequency coefficient, the abnormal image information includes the abnormal fuzzy measurement coefficient, and the working environment information includes the electromagnetic interference abnormal fluctuation coefficient. After collection, the data acquisition module calibrates the abnormal maintenance frequency coefficient, the abnormal fuzzy measurement coefficient, and the electromagnetic interference abnormal fluctuation coefficient as GA, AT, and OJ respectively;

[0048] It should be noted that the monitoring equipment mentioned above in the present invention refers to the cameras actually installed in the block, and the specific model type depends on the actual situation and will not be described in detail here;

[0049] The abnormal maintenance frequency coefficient is used to measure the stability of the working state of the monitoring equipment over a period of time. The higher the abnormal maintenance frequency coefficient, the more unstable the working state of the monitoring equipment is, and the probability of abnormal hidden dangers will be greatly increased;

[0050] A high abnormal maintenance frequency coefficient has the following adverse effects on the working status of monitoring equipment:

[0051] Instability: A high abnormal maintenance frequency factor indicates that the equipment is more likely to have problems, resulting in unstable working conditions. This may cause the monitoring system to be unable to operate normally continuously, affecting safety and reliability.

[0052] Downtime: During maintenance, monitoring equipment often needs to be shut down, which affects the continuity of the monitoring system. In some cases, downtime may lead to safety risks or loss of important data.

[0053] Data loss: If monitoring equipment is not functioning properly during maintenance, data loss may occur, which is particularly important for systems that record and track security events or abnormal conditions.

[0054] Safety risks: Frequent abnormal maintenance may result in the monitoring system being unable to provide necessary safety alarms and monitoring functions in emergency situations, thereby increasing potential safety risks.

[0055] The logic for obtaining the abnormal maintenance frequency coefficient is as follows:

[0056] Set the baseline threshold PL for monitoring equipment maintenance frequency z (The upper limit of maintenance frequency);

[0057] By analyzing the past maintenance record data of each monitoring device in the block scene and simulating multiple sets of data through software, it helps to determine the maximum upper limit of the maintenance frequency;

[0058] Get the maintenance frequency within time T and mark the maintenance frequency as PL. When PL≤PL z When PL>PL zWhen PL>PL z The maintenance frequency is marked as PL α ;

[0059] Calculate the abnormal maintenance frequency coefficient, the expression is as follows: Wherein, i represents the order number of the abnormal maintenance frequency, i={1, 2, ..., n}, n is a positive integer;

[0060] Fuzziness measurement is a numerical indicator used to quantify the degree of blurriness of images taken by monitoring equipment. The higher the abnormal fuzziness measurement coefficient, the worse the quality of the monitoring video taken by the monitoring equipment, reflecting the instability of its working state and low reliability, and the probability of abnormal hidden dangers will be greatly increased;

[0061] The logic for obtaining the abnormal fuzzy measurement coefficient is as follows:

[0062] Obtain the video data fed back by the monitoring device within T time, decompose the obtained video data to obtain a series of image frames, and number the decomposed image frames to obtain an image frame set OR = {QP j}={QP1,QP2,...,QP m}, where j represents the sequence number of each image frame, j = {1, 2, ..., m}, m is a positive integer;

[0063] The acquisition of image frame blur measurement includes the following steps:

[0064] Image preprocessing: performing preprocessing operations on the images in the image frame set, such as image denoising, brightness and contrast adjustment;

[0065] Fourier transform: Convert an image from the spatial domain (pixel domain) to the frequency domain using the Fourier transform or its variants (such as the fast Fourier transform, FFT);

[0066] Spectral analysis: In the frequency domain, the spectral components of the image are analyzed to obtain information about high and low frequencies, and the power spectral density (PSD) is used to analyze the spectrum;

[0067] Calculate the blur measure: According to the results of frequency domain analysis, the blur measure is used to quantify the blur degree of the image. The expression is as follows: LM = ∑ [(YS (v,u) - QP j (v,u))^2 / (M*N)], where M and N represent the width and height of the image, respectively, YS(v,u) and QP j (v,u) represent the preset standard image YS and the image frame QP in the image frame set respectively j The pixel value at position (v,u);

[0068] Set the baseline threshold of the fuzzy measure LM and mark it as LM z ;

[0069] It should be noted that the setting of the blur measure benchmark threshold can be set according to the suggestions provided by relevant personnel in the field of imaging;

[0070] The blur measure of each image frame in the image frame set is compared with the blur measure reference threshold. When LM≤LM z When LM>LM z When LM>LM z The fuzzy measure is denoted as LM β ;

[0071] Calculate the abnormal fuzzy measure coefficient, and its expression is as follows: in, Indicates that the kth LM>LM z Blur measure of the image frame, PO means LM>LM z The average value of the fuzzy measure, k means LM>LM z The number of blur measurement image frames, k = {1, 2, ..., h}, h is a positive integer;

[0072] Electromagnetic interference refers to the electromagnetic energy in the electromagnetic field that is transmitted to the monitoring equipment, usually in an unintentional form, interfering with the normal operation of the monitoring equipment. The higher the electromagnetic interference abnormal fluctuation coefficient, the worse the working environment of the monitoring equipment, reflecting the instability of its working state and low reliability, and the probability of abnormal hidden dangers will be greatly increased;

[0073] The logic for obtaining the electromagnetic interference abnormal fluctuation coefficient is as follows:

[0074] Set the baseline threshold range of electromagnetic intensity that the monitoring equipment can withstand when working, and mark it as DB z min ~DB z max , where DB z min Indicates the minimum value of the electromagnetic intensity that the monitoring equipment can withstand when working, DB z max Indicates the maximum value of the electromagnetic intensity that the monitoring equipment can withstand when working;

[0075] An electromagnetic field detector is installed in the block scene where the monitoring device is located to obtain the intensity of the electromagnetic field within the time T in real time, and mark it as DB;

[0076] Calculate the abnormal fluctuation coefficient of electromagnetic interference, the expression is as follows: Wherein, x represents the number of times the electromagnetic intensity in the block scene where the monitoring device is located exceeds the reference threshold range, x={1, 2, ..., y}, y is a positive integer, [θ1,θ2] are the start and end time of each time when the electromagnetic intensity exceeds the reference threshold range;

[0077] The abnormal evaluation module normalizes the acquired abnormal maintenance frequency coefficient GA, abnormal fuzzy measurement coefficient AT, and electromagnetic interference abnormal fluctuation coefficient OJ, and establishes an abnormal evaluation model to generate an abnormal hidden danger index and mark it as YH. The expression is as follows: (a3*OJ+1), where a1, a2, and a3 are preset proportional coefficients of the abnormal maintenance frequency coefficient GA, the abnormal fuzzy measurement coefficient AT, and the electromagnetic interference abnormal fluctuation coefficient OJ, respectively, and a1>a2>a3>0;

[0078] From the above calculation, it can be seen that the larger the abnormal maintenance frequency coefficient, the larger the abnormal fuzzy measurement coefficient, and the larger the electromagnetic interference abnormal fluctuation coefficient, that is, the larger the abnormal hidden danger index, the more unstable the working state of the monitoring equipment is, and the probability of its abnormal hidden danger will be greatly increased;

[0079] Compare the abnormal hidden danger index with the abnormal hidden danger index reference threshold, evaluate the monitoring equipment at different angles of the block, and screen out abnormal monitoring equipment;

[0080] If the abnormal hidden danger index is greater than or equal to the abnormal hidden danger index reference threshold, the monitoring device is marked as an abnormal operation monitoring device, indicating that the operation status of the monitoring device is not good. In subsequent operation, the probability of abnormality is high. The abnormal hidden danger assessment frequency is adjusted and the assessment information is recorded.

[0081] If the abnormal hidden danger index is less than the abnormal hidden danger index reference threshold, the monitoring device is marked as a normal operating monitoring device, indicating that the operating status of the monitoring device is good and there is no need to adjust its abnormal hidden danger assessment frequency;

[0082] After adjusting the frequency of abnormal hidden danger assessment for abnormally operating monitoring equipment, the comprehensive analysis module establishes an abnormal hidden danger index analysis set for its subsequently continuously generated abnormal hidden danger index and marks it as an abnormal hidden danger index set, whose expression is: AK = {YH q}=YH1, YH2, YH3,…,YH s}, q represents the order number of different task completion impact indexes in the abnormal hidden danger index set, z = {1, 2, ..., s}, s is a positive integer;

[0083] Obtain the abnormal hidden danger index outlier information and monitoring strategy switching time information. The abnormal hidden danger index outlier information includes YH in the abnormal hidden danger index set sThe abnormal hidden danger outlier degree value PL and the monitoring strategy switching time information include YH s The difference TS between the evaluation time point and the monitoring strategy switching time point;

[0084] The calculation expression of the abnormal hidden danger outlier degree value is as follows: Among them, PJ is the average value of the abnormal hidden danger index in the abnormal hidden danger index set, and BC is the standard deviation of the abnormal hidden danger index in the abnormal hidden danger index set;

[0085] YH s The difference between the evaluation time point and the monitoring strategy switching time point refers to the difference between the time point of the abnormal hidden danger index evaluation numbered s in the abnormal hidden danger index set and the preset time point of the next monitoring strategy switching;

[0086] The comprehensive analysis module will obtain YH s Abnormal hidden danger outlier degree value PL, YH s The difference TS between the evaluation time point and the monitoring strategy switching time point is normalized, and a risk warning model is established to generate a risk warning index marked as FX, which is expressed as follows: FX = b1*PL+b2*TS, where b1 and b2 are YH s Abnormal hidden danger outlier degree value PL, YH s The preset proportional coefficient of the difference TS between the evaluation time point and the monitoring strategy switching time point, and b1>b2>0;

[0087] The comprehensive analysis module transmits the generated risk warning index to the risk warning module;

[0088] The risk warning module compares the risk warning index with the preset risk warning index reference threshold, issues a warning at a reasonable time point, and reminds managers that the next switch of the monitoring strategy may be abnormal;

[0089] If the risk warning index is greater than or equal to the risk warning index reference threshold, a warning signal is generated, indicating that the next switch of the monitoring strategy may be abnormal, and time is reserved for management personnel to carry out relevant processing;

[0090] If the risk warning index is less than the risk warning index reference threshold, there is no need to generate a warning signal and continue to conduct risk assessment on the equipment.

[0091] The present invention evaluates different monitoring devices in the block by acquiring historical maintenance information, feedback abnormal image information and working environment information of monitoring devices, screens out monitoring devices that may have abnormal hidden dangers, continuously tracks the status of monitoring devices, establishes an abnormal hidden danger index set, and obtains YH in the abnormal hidden danger index set. s Abnormal hidden danger outlier degree value, YHs The difference between the evaluation time point and the monitoring strategy switching time point is used to generate a risk warning index. The risk warning index is compared with the risk warning index reference threshold, and a warning is issued at a reasonable time point to prompt management personnel to make corresponding processing to avoid serious lags in the next scheduled and real-time monitoring strategy switching, and to ensure that the system can analyze the monitoring data of the monitored area in a timely manner, so that when an abnormal situation occurs in this area, a timely warning can be obtained.

[0092] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0093] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0094] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0095] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0096] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0097] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0098] The above is only 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 who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A spatial area intelligent monitoring safety warning system for digital blocks, characterized by: It includes data collection module, abnormal assessment module, comprehensive analysis module and risk warning module, and each module is connected through signals; The data acquisition module is used to collect the historical maintenance information of the monitoring equipment, the feedback abnormal image information and the working environment information. After the collection, the historical maintenance information, the abnormal image information and the working environment information are transmitted to the abnormal assessment module; The abnormality assessment module is used to normalize the historical maintenance information, abnormal image information and working environment information, establish an abnormality assessment model, generate an abnormal hidden danger index, and compare the abnormal hidden danger index with the abnormal hidden danger index reference threshold, evaluate the monitoring equipment at different angles of the block, and screen the monitoring equipment in abnormal conditions; A comprehensive analysis module is used to establish an abnormal hidden danger index analysis set based on the abnormal hidden danger index subsequently generated by the abnormal monitoring equipment, and to establish a risk warning model and generate a risk warning index by obtaining the abnormal hidden danger index outlier information and monitoring strategy switching time information; The risk warning module is used to compare the risk warning index with the risk warning index reference threshold and select whether to generate a warning signal; The historical maintenance information includes the abnormal maintenance frequency coefficient, the abnormal image information includes the abnormal fuzzy measurement coefficient, and the working environment information includes the electromagnetic interference abnormal fluctuation coefficient. After collection, the data acquisition module calibrates the abnormal maintenance frequency coefficient, the abnormal fuzzy measurement coefficient, and the electromagnetic interference abnormal fluctuation coefficient as GA, AT, and OJ respectively; The logic for obtaining the abnormal maintenance frequency coefficient is as follows: Set the baseline threshold PL for monitoring equipment maintenance frequency z ; Get the maintenance frequency within time T and mark the maintenance frequency as PL. When PL>PL z When PL>PL z The maintenance frequency is marked as PL α ; Calculate the abnormal maintenance frequency coefficient, the expression is as follows: Wherein, i represents the order number of the abnormal maintenance frequency, i={1, 2, . . . , n}, and n is a positive integer.

2. The spatial area intelligent monitoring safety early warning system for a digital block according to claim 1 is characterized by: The logic for obtaining the abnormal fuzzy measurement coefficient is as follows: Obtain the video data fed back by the monitoring device within T time, decompose the obtained video data to obtain a series of image frames, and number the decomposed image frames to obtain an image frame set OR = {QP j }={QP1,QP2,...,QP m }, where j represents the sequence number of each image frame, j = {1, 2, ..., m}, m is a positive integer; Set the baseline threshold of the fuzzy measure LM and mark it as LM z ; The blur measure of each image frame in the image frame set is compared with the blur measure reference threshold. When LM>LM z When LM>LM z The fuzzy measure is denoted as LM β ; Calculate the abnormal fuzzy measure coefficient, and its expression is as follows: in, Indicates that the kth LM>LM z Blur measure of the image frame, PO means LM>LM z The average value of the fuzzy measure, k means LM>LM z The number of blur measurement image frames, k = {1, 2, ..., h}, h is a positive integer.

3. The spatial area intelligent monitoring safety early warning system for digital blocks according to claim 2 is characterized by: The acquisition of the image frame blur measure LM includes the following steps: Image preprocessing: performing preprocessing operations on images in an image frame set; Fourier transform: Convert an image from the spatial domain (pixel domain) to the frequency domain using Fourier transform; Spectral analysis: In the frequency domain, the spectral components of the image are analyzed to obtain high-frequency and low-frequency information; Calculate the blur measure: According to the results of frequency domain analysis, the blur measure is used to quantify the blur degree of the image. The expression is as follows: LM = ∑ [(YS (v,u) - QP j (v,u))^2 / (M*N)], where M and N represent the width and height of the image, respectively, YS(v,u) and QP j (v,u) represent the preset standard image YS and the image frame QP in the image frame set respectively j The pixel value at position (v,u).

4. The spatial area intelligent monitoring safety warning system for a digital block according to claim 1 is characterized by: The logic for obtaining the electromagnetic interference abnormal fluctuation coefficient is as follows: Set the baseline threshold range of electromagnetic intensity that the monitoring equipment can withstand when working, and mark it as DB z min ~DB z max , where DB z min Indicates the minimum value of the electromagnetic intensity that the monitoring equipment can withstand when working, DB z max Indicates the maximum value of the electromagnetic intensity that the monitoring equipment can withstand when working; An electromagnetic field detector is installed in the block scene where the monitoring device is located to obtain the intensity of the electromagnetic field within the time T in real time, and mark it as DB; Calculate the abnormal fluctuation coefficient of electromagnetic interference, the expression is as follows: Wherein, x represents the number of times the electromagnetic intensity in the block scene where the monitoring device is located exceeds the reference threshold range, x={1, 2, ..., y}, y is a positive integer, [θ1,θ2] is the start time and end time of each time the electromagnetic intensity exceeds the reference threshold range.

5. According to claim 1, a spatial area intelligent monitoring safety warning system for digital blocks is characterized by: The abnormal evaluation module normalizes the acquired abnormal maintenance frequency coefficient GA, abnormal fuzzy measurement coefficient AT, and electromagnetic interference abnormal fluctuation coefficient OJ, and establishes an abnormal evaluation model to generate an abnormal hidden danger index and mark it as YH. The expression is as follows: YH = Wherein, a1, a2, and a3 are preset proportional coefficients of abnormal maintenance frequency coefficient GA, abnormal fuzzy measurement coefficient AT, and electromagnetic interference abnormal fluctuation coefficient OJ, respectively, and a1>a2>a3>0; Compare the abnormal hidden danger index with the abnormal hidden danger index reference threshold to screen out abnormal monitoring equipment; If the abnormal hidden danger index is greater than or equal to the abnormal hidden danger index reference threshold, the monitoring device is marked as an abnormal operation monitoring device.

6. The spatial area intelligent monitoring safety warning system for digital blocks according to claim 5 is characterized by: The comprehensive analysis module establishes an abnormal hidden danger index analysis set based on the abnormal hidden danger index subsequently generated by the abnormal monitoring equipment, and establishes a risk warning model and generates a risk warning index by obtaining the outlier information of the abnormal hidden danger index and the switching time information of the monitoring strategy.

7. The spatial area intelligent monitoring safety warning system for digital blocks according to claim 6 is characterized by: A risk warning module compares the risk warning index with a risk warning index reference threshold; If the risk warning index is greater than or equal to the risk warning index reference threshold, a warning signal is generated; If the risk warning index is less than the risk warning index reference threshold, there is no need to generate a warning signal.

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