Alarm monitoring system for safe operation of high-voltage switch cabinet

Abstract

The invention relates to the technical field of high-voltage switch cabinets, in particular to an alarm monitoring system for safe operation of a high-voltage switch cabinet. Comprising a central control unit used for setting a plurality of monitoring points according to equipment parameters; the monitoring unit comprises a plurality of monitoring sub-modules, and the monitoring sub-modules are arranged at all monitoring points; the alarm unit is used for generating an alarm signal; the central control unit comprises a first processing module used for establishing a risk early warning model; the second processing module is used for setting a monitoring strategy of the monitoring unit and acquiring a monitoring data packet according to the monitoring strategy; the second processing module is further used for judging whether an early warning instruction is generated or not according to the monitoring data packet; and the first control module is used for setting working parameters of the alarm unit according to the early warning instruction, comprehensively monitoring the operation process by establishing a multi-angle monitoring and alarm reminding network, timely performing early warning on electrical, mechanical and environmental risks in the operation process, and ensuring personnel safety.

Description

Technical Field

[0001] This application relates to the technical field of high-voltage switchgear, and particularly to an alarm monitoring system for the safe operation of high-voltage switchgear. Background Art

[0002] As a key device in the power system, the safety measures during the operation of high-voltage switchgear still rely on the subjective judgment of personnel. Although the five-prevention measures are included in the operation, the verification and power-on of the switchgear after maintenance still rely on human judgment. When both the operator and the supervisor have technical shortcomings and misjudgments due to poor mood, personal and equipment injuries will occur.

[0003] At the same time, only static information is provided during the operation monitoring of high-voltage switchgear, and dynamic risks such as arc and overheating cannot be predicted. If an abnormality occurs during the operation (such as mechanism jamming, partial discharge), there is no real-time alarm mechanism, which is likely to lead to the expansion of the accident. Summary of the Invention

[0004] The purpose of this application is: To solve the above technical problems, this application provides an alarm monitoring system for the safe operation of high-voltage switchgear, aiming to monitor the electrical, mechanical and environmental risks during the operation in real time, improve the early warning efficiency, and prevent accidents such as misoperation, electric shock, and arc injury.

[0005] In some embodiments of this application, an alarm monitoring system for the safe operation of high-voltage switchgear is provided, including: A central control unit for setting multiple monitoring points according to equipment parameters; A monitoring unit including multiple monitoring sub-modules, and the monitoring sub-modules are arranged at each monitoring point; An alarm unit for generating an alarm signal; The central control unit includes: A first processing module for establishing a risk early warning model; A second processing module for setting the monitoring strategy of the monitoring unit and obtaining a monitoring data packet according to the monitoring strategy; The second processing module is also used to judge whether to generate a warning instruction according to the monitoring data packet; A first control module for setting the working parameters of the alarm unit according to the warning instruction.

[0006] In some embodiments of this application, the first processing module is also used for: Establishing a behavior sub-model based on historical monitoring data; Traversing the identity parameters of all personnel; Establishing a staff sequence A, A=(a1, a2…a i …a n ), where, ai is the i-th staff member; n is the number of staff members; Set the first-level risk sub-model according to the staff sequence A and the behavior sub-model; Set multiple risk characteristic indicators according to historical monitoring data; Generate a second-level risk sub-model based on all risk characteristic indicators; Construct a risk early warning model based on the first-level risk sub-model and the second-level risk sub-model.

[0007] In some embodiments of the present application, when setting the first-level risk sub-model, it includes: Set a according to staff member A in turn i is the target staff member; Generate a training data packet for the target staff member according to historical monitoring data; Generate a compensation sub-strategy for the target staff member according to the training data packet; Generate compensation sub-strategies for each staff member in turn; Establish a compensation sub-strategy sequence P, P = (p1, p2... p i ... p n ), where p i is the compensation sub-strategy of the i-th staff member; n is the number of staff members; Set the first-level risk sub-model according to the compensation sub-strategy sequence P and the behavior sub-model.

[0008] In some embodiments of the present application, the second processing module further includes: Establish a monitoring point sequence B, B = (b1, b2... b i ... b m ), where b i is the i-th monitoring point; m is the number of monitoring points; Judge whether there is an operation plan; If it exists, the second processing module generates a first-level monitoring strategy; If it does not exist, the second processing module generates a second-level monitoring strategy.

[0009] In some embodiments of the present application, the second-level monitoring strategy includes: Establish a first-level monitoring point sequence B1 according to the position parameters of each monitoring point, B1 = (b 11 , b 12 ... b 1i ... b 1m1 ), where b 1i is the i-th first-level monitoring point; m1 is the number of first-level monitoring points; Set the first-level working parameters of the monitoring sub-modules corresponding to each first-level monitoring point.

[0010] In some embodiments of the present application, the primary monitoring strategy includes: Obtain the operation cycle and operation content according to the operation plan; Set the monitoring cycle according to the operation cycle, and multiple feedback time nodes are included within the monitoring cycle; Set the end time node of each time interval as the feedback time node; Generate an associated evaluation value related to each monitoring point according to the operation content; Establish an associated evaluation value sequence W, W = (w1, w2…w i …w m ), where w i is the associated evaluation value of the operation content and the i-th monitoring point; m is the number of monitoring points; Preset an associated evaluation value threshold W1; If w i > W1, set the i-th monitoring point as an associated monitoring point; Obtain the monitoring data packets of each associated monitoring point according to the preset feedback time node.

[0011] In some embodiments of the present application, when judging whether to generate a warning instruction according to the monitoring data packet, it includes: Obtain the monitoring data packets of all associated monitoring points at the current feedback time node; Generate a behavior data packet and a feature parameter packet according to the preprocessing result; Generate a behavior risk value f1 at the current feedback time node according to the primary risk sub-model and the behavior data packet; Generate a status risk value f2 at the current feedback time node according to the secondary risk sub-model and the feature data packet; Generate the current risk warning value c according to the behavior risk value f and the status risk value d; Preset a risk warning value threshold C1; If c > C1, generate a primary warning instruction at the current feedback time node.

[0012] In some embodiments of the present application, when generating the behavior risk value f at the current feedback time node, it includes: Generate a personnel feature packet according to the monitoring data packets of all associated points; Set a primary compensation strategy according to the personnel feature packet; f = e1×Q1× η i ×Y1(i)×(k i - k' i )]+ e2×Q2× η i ×Y1(i)×(k i - g i ×k'i ); where, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; θ1 is the number of behavioral risk indicators; η i is the influence factor of the i-th behavioral risk indicator; k i is the real-time reference value of the i-th behavioral risk indicator at the current feedback time node; k' i is the standard reference value of the i-th behavioral risk indicator; Y1(i) is a selection coefficient; if (k i -k' i ) > 0, Y1(i) = 1; if (k i -k' i ) < 0, Y1(i) = 0; g i is the correction coefficient for setting the i-th behavioral risk indicator based on the first-level compensation strategy.

[0013] In some embodiments of the present application, when generating the state risk value d at the current feedback time node, it includes: d = e3 × Q3 × βi × Y2(i) × (d i -d' i ) 2 + e4 × Q4 × j i ×h i ; where, e3 is a preset third weight coefficient; e4 is a preset fourth weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset fourth fixed coefficient; is the number of risk characteristic indicators; β i is the influence factor of the i-th risk characteristic indicator; d i is the reference value of the i-th risk characteristic indicator at the current feedback time node; d' i is the safety threshold of the i-th risk characteristic indicator; Y2(i) is a selection coefficient; if (d i -d' i ) > 0, Y2(i) = 1; if (d i -d' i ) < 0, Y2(i) = 0; is the number of historical evaluation indicators; j i is the influence factor of the i-th historical evaluation indicator; hi is the reference value of the i-th historical evaluation indicator at the current feedback time node.

[0014] Compared with the prior art, the beneficial effects of an alarm monitoring system for safe operation of high-voltage switchgear in the embodiments of the present application are as follows: By establishing a multi - angle monitoring and alarm reminder network, comprehensively monitor the operation process of each staff member in a timely manner. Combining the comprehensive analysis results of personnel behavior and risk characteristic indicators, give early warnings of electrical, mechanical and environmental risks during the operation process in a timely manner to ensure personnel safety.

[0015] By analyzing the historical behaviors of each staff member, set corresponding compensation strategies, so as to improve the targeted monitoring of each staff member, improve the recognition efficiency of risk behaviors, and prevent accidents such as misoperations, electric shocks, and arc injuries. Brief Description of the Drawings

[0016] Figure 1 It is a schematic structural diagram of an alarm monitoring system for the safe operation of a high - voltage switchgear in a preferred embodiment of the embodiment of the present application. Detailed Embodiments

[0017] The following will further describe in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0018] In the description of the present application, it should be understood that the orientation or positional relationships indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present application.

[0019] The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0020] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0021] Such as Figure 1As shown in the figure, an alarm monitoring system for the safe operation of a high-voltage switchgear in a preferred embodiment of the present application includes: A central control unit for setting multiple monitoring points according to device parameters; A monitoring unit including multiple monitoring sub-modules, and the monitoring sub-modules are arranged at each monitoring point; An alarm unit for generating an alarm signal; The central control unit includes: A first processing module for establishing a risk early warning model; A second processing module for setting the monitoring strategy of the monitoring unit and obtaining monitoring data packets according to the monitoring strategy; The second processing module is also used for judging whether to generate a warning instruction according to the monitoring data packet; A first control module for setting the working parameters of the alarm unit according to the warning instruction.

[0022] Specifically, the monitoring unit is preferably various sensors and image acquisition devices, and the specific devices selected for the corresponding monitoring sub-modules are different according to the different data types required to be collected at each monitoring point.

[0023] Specifically, the alarm unit includes multiple alarm sub-modules and can perform sound alarms and light alarms.

[0024] Specifically, the first processing module is also used for: Establishing a behavior sub-model based on historical monitoring data; Traversing the identity parameters of all personnel; Establishing a sequence of staff A, A = (a1, a2... a i …a n ), where a i is the i-th staff member; n is the number of staff members; Setting a primary risk sub-model according to the sequence of staff A and the behavior sub-model; Setting multiple risk characteristic indicators according to historical monitoring data; Generating a secondary risk sub-model according to all risk characteristic indicators; Constructing a risk early warning model according to the primary risk sub-model and the secondary risk sub-model.

[0025] Specifically, the historical monitoring data refers to the historical operation data and accident data of the high-voltage switchgear recorded.

[0026] Specifically, the primary risk sub-model mainly identifies and analyzes the operation behavior of staff by obtaining monitoring image data and generates corresponding behavior risk values.

[0027] Specifically, the secondary risk sub-model mainly generates corresponding state risk values by analyzing the real-time operation parameters and environmental parameters of the high-voltage switchgear (for example, no water accumulation, humidity, sundries or flammable substances around the switchgear, ambient temperature, humidity, the switchgear is in a power-off state, no abnormal sounds (such as discharge, arc sound), no smoke or sparks, motor overload, refusal to operate, etc.).

[0028] Specifically, when setting the primary risk sub-model, it includes: Set a i as the target staff member in sequence according to staff member A; Generate a training data packet for the target staff member based on historical monitoring data; Generate a compensation sub-strategy for the target staff member based on the training data packet; Generate compensation sub-strategies for each staff member in sequence; Establish a sequence of compensation sub-strategies P, P = (p1, p2…p i …p n ), where p i is the compensation sub-strategy for the i-th staff member; n is the number of staff members; Set the primary risk sub-model according to the sequence of compensation sub-strategies P and the behavior sub-model.

[0029] Specifically, set multiple behavioral risk indicators according to the behavior sub-model. It includes but is not limited to multiple parameters such as whether the operation sequence is correct, whether non-operation areas are touched, and whether there are any violation behaviors.

[0030] Specifically, the training data packet includes the historical operation parameters of the target staff member. By analyzing the data in the training data packet, the compensation sub-strategy of the target staff member is set to achieve dynamic adjustment of the safety thresholds for each behavioral risk indicator, improving the early warning efficiency for operation risks.

[0031] Specifically, the compensation sub-strategy includes compensation coefficients for the safety thresholds of each behavioral risk indicator. The greater the probability of operation risks occurring during the historical operation of the staff member, the smaller the corresponding compensation coefficient, and the value range of all compensation coefficients is between zero and one.

[0032] It can be understood that in the above embodiments, by analyzing the historical behaviors of each staff member, corresponding compensation strategies are set, thereby improving the targeted monitoring of each staff member, enhancing the identification efficiency of risk behaviors, and preventing accidents such as misoperations, electric shocks, and arc injuries.

[0033] In the preferred embodiment of the embodiment of the present application, the second processing module further includes: Establish a sequence of monitoring points B, B = (b1, b2…b i …bm ), where b i is the i-th monitoring point; m is the number of monitoring points; Determine whether there is an operation plan; If there is, the second processing module generates a primary monitoring strategy; If not, the second processing module generates a secondary monitoring strategy.

[0034] Specifically, the operation plan refers to whether the high-voltage switchgear needs to be operated at the current time node.

[0035] Specifically, the secondary monitoring strategy includes: Establish a primary monitoring point sequence B1 according to the position parameters of each monitoring point, B1 = (b 11 , b 12 …b 1i …b 1m1 ), where b 1i is the i-th primary monitoring point; m1 is the number of primary monitoring points; Set the primary working parameters of the monitoring sub-module corresponding to each primary monitoring point.

[0036] Specifically, the primary monitoring point is the induction point set in front of each high-voltage switchgear.

[0037] Specifically, the primary working parameter means that when a person stays in front of the switchgear for 5 seconds, the name and number of the switchgear are alarmed by sound, and the operating state or standby state of the switchgear, and the live conditions of the static contacts at the upper and lower ports of the switchgear are described and warned.

[0038] Specifically, the primary monitoring strategy includes: Obtain the operation cycle and operation content according to the operation plan; Set the monitoring cycle according to the operation cycle, and multiple feedback time nodes are included in the monitoring cycle; Set the end time node of each time interval as the feedback time node; Generate the associated evaluation value with each monitoring point according to the operation content; Establish an associated evaluation value sequence W, W = (w1, w2…w i …w m ), where w i is the associated evaluation value of the operation content and the i-th monitoring point; m is the number of monitoring points; Preset the associated evaluation value threshold W1; If w i > W1, set the i-th monitoring point as the associated monitoring point; Obtain the monitoring data packets of each associated monitoring point according to the preset feedback time node.

[0039] Specifically, according to the operation content, determine the position points to be operated, and determine whether each monitoring point can collect valid data. If valid data can be collected, it is an associated monitoring point.

[0040] It can be understood that in the above embodiments, by dynamically adjusting the working parameters of each monitoring point, while ensuring the monitoring efficiency, the overall operation and maintenance cost is reduced.

[0041] In the preferred embodiment of the present application, when determining whether to generate a warning instruction according to the monitoring data packet, it includes: Obtain the monitoring data packets of all associated monitoring points at the current feedback time node; Generate a behavior data packet and a feature parameter packet according to the preprocessing result; Generate a behavior risk value f1 at the current feedback time node according to the first-level risk sub-model and the behavior data packet; Generate a state risk value f2 at the current feedback time node according to the second-level risk sub-model and the feature data packet; Generate the current risk warning value c according to the behavior risk value f and the state risk value d; Preset a risk warning value threshold C1; If c > C1, a first-level warning instruction is generated at the current feedback time node.

[0042] Specifically, the risk warning value threshold can be set according to historical parameters.

[0043] Specifically, the first-level warning instruction means that there is a safety risk in the current high-voltage switch cabinet, and corresponding voice alarm signals and light alarm signals need to be generated in time through the alarm unit to remind relevant staff and avoid personal injury accidents.

[0044] Specifically, when generating the behavior risk value f at the current feedback time node, it includes: Generate a personnel feature packet according to the monitoring data packets of all associated points; Set a first-level compensation strategy according to the personnel feature packet; f = e1 × Q1 × η i × Y1(i) × (k i - k' i ) + e2 × Q2 × η i × Y1(i) × (k i - g i × k' i ); Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; θ1 is the number of behavior risk indicators; η iis the influencing factor of the i-th behavioral risk indicator; k i is the real-time reference value of the i-th behavioral risk indicator at the current feedback time node; k' i is the standard reference value of the i-th behavioral risk indicator; Y1(i) is the selection coefficient; if (k i - k' i ) > 0, Y1(i) = 1; if (k i - k' i ) < 0, Y1(i) = 0; g i is the correction coefficient for setting the i-th behavioral risk indicator based on the first-level compensation strategy.

[0045] Specifically, all parameters in the model are normalized by presetting the first fixed coefficient and the second fixed coefficient, so that each parameter in the model is within the same value range.

[0046] Specifically, the influencing factor of each behavioral risk indicator can be set according to the corresponding potential possibility of operational risk occurrence. The greater the potential possibility, the greater the corresponding influencing factor.

[0047] Specifically, when generating the state risk value d at the current feedback time node, it includes: d = e3 × Q3 × βi × Y2(i) × (d i - d' i ) 2 + e4 × Q4 × j i × h i ; Among them, e3 is the preset third weight coefficient; e4 is the preset fourth weight coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; is the number of risk characteristic indicators; β i is the influencing factor of the i-th risk characteristic indicator; d i is the reference value of the i-th risk characteristic indicator at the current feedback time node; d' i is the safety threshold of the i-th risk characteristic indicator; Y2(i) is the selection coefficient; if (d i - d' i ) > 0, Y2(i) = 1; if (d i - d' i ) < 0, Y2(i) = 0; is the number of historical evaluation indicators; j i is the influencing factor of the i-th historical evaluation indicator; hi is the reference value of the i-th historical evaluation indicator at the current feedback time node.

[0048] Specifically, the influence factors of each risk characteristic index are set according to their influence degree on the safe operation of the switchgear. The greater the influence degree, the greater the corresponding influence factor.

[0049] Specifically, the risk characteristic indicators include, but are not limited to, no water accumulation, dampness, sundries or flammable substances around the switchgear, ambient temperature, humidity, the switchgear being in a power-off state, no abnormal sounds (such as discharge, arc sounds), the presence or absence of smoke and sparks, parameters such as motor overload and refusal to operate. By quantifying each risk characteristic indicator, all-round analysis, monitoring and timely warning of electrical, mechanical and environmental risks during the operation process are realized.

[0050] Specifically, all parameters in the model are normalized by presetting a third fixed coefficient and a fourth fixed coefficient, so that each parameter in the model is within the same value range.

[0051] According to the first concept of this application, by establishing a multi-angle monitoring and alarm reminder network, the operation process of each staff member is comprehensively monitored in a timely manner. Combining the comprehensive analysis results of personnel behavior and risk characteristic indicators, electrical, mechanical and environmental risks during the operation process are warned in a timely manner to ensure personnel safety.

[0052] According to the second concept of this application, by analyzing the historical behaviors of each staff member, corresponding compensation strategies are set, so as to improve the targeted monitoring of each staff member, improve the recognition efficiency of risk behaviors, and prevent accidents such as misoperation, electric shock and arc injury.

[0053] The above are only the preferred embodiments of this application. It should be noted that for those of ordinary skill in the art of this technology, without departing from the technical principle of this application, several improvements and replacements can still be made, and these improvements and replacements should also be regarded as the protection scope of this application.

Claims

1. An alarm monitoring system for the safe operation of high-voltage switchgear, characterized in that, It includes: A central control unit for setting multiple monitoring points according to device parameters; A monitoring unit including multiple monitoring sub-modules, and the monitoring sub-modules are set at each monitoring point; An alarm unit for generating an alarm signal; The central control unit includes: A first processing module for establishing a risk early warning model; A second processing module for setting the monitoring strategy of the monitoring unit and obtaining monitoring data packets according to the monitoring strategy; The second processing module is also used for judging whether to generate a warning instruction according to the monitoring data packet; A first control module for setting the working parameters of the alarm unit according to the warning instruction.

2. The alarm monitoring system for safe operation of high-voltage switchgear according to claim 1, characterized in that, The first processing module is also used for: Establishing a behavior sub-model based on historical monitoring data; Traversing the identity parameters of all personnel; Establish a sequence of staff A, A = (a1, a2... a i … a n ), where a i is the i-th staff member; n is the number of staff members; Setting a primary risk sub-model according to the staff sequence A and the behavior sub-model; Setting multiple risk characteristic indicators according to historical monitoring data; Generating a secondary risk sub-model according to all risk characteristic indicators; Constructing a risk early warning model according to the primary risk sub-model and the secondary risk sub-model.

3. The alarm monitoring system for safe operation of high-voltage switchgear according to claim 2, wherein, When setting the primary risk sub-model, it includes: Set a successively according to staff member A i as the target staff member; Generating a training data packet of the target staff according to historical monitoring data; Generating a compensation sub-strategy of the target staff according to the training data packet; Generating the compensation sub-strategies of each staff in turn; Establish a compensation sub-strategy sequence P, P = (p1, p2…p i …p n ), where p i is the compensation sub-strategy of the i-th staff member; n is the number of staff members; Setting the primary risk sub-model according to the compensation sub-strategy sequence P and the behavior sub-model.

4. The alarm monitoring system for safe operation of high-voltage switchgear according to claim 3, characterized in that, The second processing module also includes: Establish a sequence of monitoring points B, B = (b1, b2…b i …b m ), where b i is the i-th monitoring point; m is the number of monitoring points; Judging whether there is an operation plan; If there is, the second processing module generates a primary monitoring strategy; If not, the second processing module generates a secondary monitoring strategy.

5. The alarm monitoring system for safe operation of high-voltage switchgear according to claim 4, wherein The secondary monitoring strategy includes: Establish a first-level monitoring point sequence B1 according to the position parameters of each monitoring point. B1 = (b 11 , b 12 … b 1i … b 1m1 ), where b 1i is the i-th first-level monitoring point; m1 is the number of first-level monitoring points; Setting the primary working parameters of the monitoring sub-modules corresponding to each primary monitoring point.

6. The alarm monitoring system for the safe operation of high-voltage switchgear as described in claim 4, wherein The primary monitoring strategy includes: Obtaining the operation cycle and operation content according to the operation plan; Setting the monitoring cycle according to the operation cycle, and multiple feedback time nodes are included in the monitoring cycle; Setting the end time node of each time interval as the feedback time node; Generating an associated evaluation value with each monitoring point according to the operation content; Establish a correlated evaluation value sequence W, W = (w1, w2…w i …w m ), where w i is the correlated evaluation value of the operation content and the i-th monitoring point; m is the number of monitoring points; Presetting an associated evaluation value threshold W1; If w i > W1, set the i-th monitoring point as an associated monitoring point; Obtaining the monitoring data packets of each associated monitoring point according to the preset feedback time node.

7. The alarm monitoring system for the safe operation of high-voltage switchgear according to claim 6, characterized in that, When judging whether to generate a warning instruction according to the monitoring data packet, it includes: Obtaining the monitoring data packets of all associated monitoring points at the current feedback time node; Generating a behavior data packet and a characteristic parameter packet according to the preprocessing result; Generating a behavior risk value f1 at the current feedback time node according to the primary risk sub-model and the behavior data packet; Generating a status risk value f2 at the current feedback time node according to the secondary risk sub-model and the characteristic data packet; Generating a current risk early warning value c according to the behavior risk value f and the status risk value d; Presetting a risk early warning value threshold C1; If c > C1, a primary warning instruction is generated at the current feedback time node.

8. The alarm monitoring system for the safe operation of high-voltage switchgear according to claim 7, characterized in that, When generating the behavior risk value f at the current feedback time node, it includes: Generating a personnel characteristic packet according to the monitoring data packets of all associated points; Setting a primary compensation strategy according to the personnel characteristic packet; f = e1 × Q1 × η i × Y1(i) × (k i - k' i ) + e2 × Q2 × η i × Y1(i) × (k i - g i × k' i ) Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; θ1 is the number of behavior risk indicators; η i is the influence factor of the i-th behavior risk indicator; k i is the real-time reference value of the i-th behavior risk indicator at the current feedback time node; k' i is the standard reference value of the i-th behavior risk indicator; Y1(i) is a selection coefficient; if (k i - k' i ) > 0, Y1(i) = 1; if (k i - k' i ) < 0, Y1(i) = 0; g i is the correction coefficient of the i-th behavior risk indicator set based on the primary compensation strategy.

9. The alarm monitoring system for the safe operation of high-voltage switchgear according to claim 7, characterized in that, When generating the status risk value d at the current feedback time node, it includes: d = e3×Q3× βi×Y2(i)×(d i - d' i ) 2 + e4×Q4× j i ×h i ; Among them, e3 is a preset third weight coefficient; e4 is a preset third weight coefficient; Q3 is a preset third fixed coefficient; Q4 is a preset third fixed coefficient; is the number of risk characteristic indicators; β i is the influence factor of the i-th risk characteristic indicator; d i is the reference value of the i-th risk characteristic indicator at the current feedback time node; d' i is the safety threshold of the i-th risk characteristic indicator; Y2(i) is a selection coefficient; if (d i - d' i ) > 0, Y2(i) = 1; if (d i - d' i ) < 0, Y2(i) = 0; is the number of historical evaluation indicators; j i is the influence factor of the i-th historical evaluation indicator; hi is the reference value of the i-th historical evaluation indicator at the current feedback time node.

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