Personnel safety management and control system and method used in power plant area

Abstract

The invention relates to the technical field of power plant safety management, in particular to a personnel safety management and control system and method used in a power plant area. Comprising a monitoring unit which comprises a plurality of monitoring modules and a plurality of sensing modules; the monitoring module is arranged at a monitoring point and is used for acquiring image data of each monitoring point; the sensing module is used for collecting monitoring index data of each worker; wherein each sensing module comprises a positioning sub-module, a pressure sub-module and a smoke sub-module; a sensing module worn by a person is constructed based on UWB and Beidou positioning technologies, so that position data, pressure data and all-around smoke concentration data of the person in a factory are monitored in real time, a plurality of monitoring modules are arranged in the factory, posture and motion amplitude data of the worker are collected, illegal behaviors are recognized and early warned in time, and the safety of the worker is improved. The safety of operators in the power plant is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of power plant safety management, and in particular to a personnel safety management and control system and method for use in a power plant area. Background Art

[0002] With the development of industrial automation and intelligence, safety control has become a key focus area in high-risk industries such as power plants. Personnel safety is an important part of power plant operations, and ensuring that employees follow safety regulations during work is the key to reducing the incidence of accidents.

[0003] At present, the safety of personnel operations is mainly monitored through sensor technology, traditional video surveillance technology, image recognition technology, etc., but the real-time and accuracy are low, and cannot meet the power plant's real-time intelligent monitoring needs for various illegal operations. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides a personnel safety management and control system and method for power plant areas, aiming to improve the monitoring efficiency within the power plant area, timely warn of violations, and ensure the safety of workers in the power plant.

[0005] In some embodiments of the present application, a sensor module worn by personnel is constructed based on UWB and Beidou positioning technology, so as to perform real-time monitoring of the position data, pressure data and surrounding smoke concentration data of personnel in the factory area, and set up multiple monitoring modules in the factory area to collect the posture and movement amplitude data of the staff, so as to identify and warn of violations in a timely manner and ensure the safety of workers in the power plant.

[0006] In some embodiments of the present application, by establishing a monitoring cycle, corresponding monitoring plans are set for all personnel entering the factory within a single cycle. At the same time, the real-time location data of each person is used to call the corresponding monitoring module to collect the personnel posture data, so as to achieve accurate monitoring of individual personnel, reduce the operation and maintenance cost of the monitoring module, and improve its monitoring efficiency.

[0007] In some embodiments of the present application, a personnel safety management and control system for a power plant area is provided, comprising:

[0008] The central control unit is used to establish multiple monitoring points based on the equipment parameters within the plant;

[0009] A monitoring unit, comprising a plurality of monitoring modules and a plurality of sensor modules;

[0010] The monitoring module is arranged at the monitoring point and is used to collect image data of each monitoring point;

[0011] The sensor module is used to collect monitoring index data of each staff member;

[0012] Wherein, a single sensing module includes: a positioning submodule, a pressure submodule and a smoke submodule;

[0013] The central control unit comprises:

[0014] The first processing module is used to establish a personnel management database and generate identity tags and identification tags for each staff member;

[0015] The second processing module is used to establish an identity tag-sensing module mapping table;

[0016] The third processing module is used to establish multiple monitoring cycles and set the working parameters of the monitoring unit in each monitoring cycle according to the work plan;

[0017] The fourth processing module is used to obtain all image data and all monitoring indicator data collected by the monitoring unit according to a preset feedback time node, and determine whether to generate an early warning instruction;

[0018] The fifth processing module is used to establish a monitoring point sequence B, B = (b1, b2...bi...bm), where bi is the i-th monitoring point and m is the number of monitoring points.

[0019] In some embodiments of the present application, the third processing module is further used to:

[0020] Obtain the work plan within the current monitoring cycle according to the preset monitoring time node;

[0021] Construct a sequence A of personnel waiting to enter the factory, A = (a1, a2...ai...an), where ai is the i-th personnel waiting to enter the factory in the current monitoring period; n is the i-th personnel waiting to enter the factory in the current monitoring period;

[0022] Select multiple primary sensor modules according to the number sequence A of people to enter the factory and the identity tag-sensor module mapping table;

[0023] Establish a primary sensor module sequence K, K = (k1, k2...ki...kn), where ki is the sensor module corresponding to the i-th person to enter the factory in the current monitoring period;

[0024] Set the feedback time nodes of each primary sensor module in turn.

[0025] In some embodiments of the present application, when the feedback time nodes of each primary sensor module are set in sequence, it includes:

[0026] According to the first-level sensor module sequence K, ki is set as the target first-level sensor module in sequence;

[0027] Generate the expected action trajectory of the target primary sensor module according to the work plan in the current monitoring cycle;

[0028] Obtain the monitoring evaluation value y of the personnel to be admitted to the factory corresponding to the target primary sensor module;

[0029] Set the feedback cycle duration t according to the monitoring evaluation value y;

[0030] Set multiple feedback time nodes of the target primary sensor module according to the expected activity trajectory and feedback cycle duration;

[0031] The target first-level sensor module obtains real-time monitoring data according to the feedback time node;

[0032] Set the feedback time nodes of each primary sensor module in turn.

[0033] In some embodiments of the present application, when generating the monitoring evaluation value y, it includes:

[0034]

[0035] Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of historical evaluation indicators; η 1i is the impact factor of the i-th historical evaluation index; c i is the reference value of the i-th historical evaluation index of the personnel to be entered into the factory corresponding to the target primary sensor module; θ2 is the number of trajectory evaluation indicators; η 2i is the influencing factor of the i-th trajectory evaluation index; j i is the reference value of the evaluation index of the i-th trajectory generated based on the expected action trajectory.

[0036] In some embodiments of the present application, the fourth processing module is further used to:

[0037] Generate risk assessment values ​​for each primary sensor module in sequence;

[0038] Establish a risk assessment value sequence F, F = (f1, f2...fi...fn), where fi is the risk assessment value corresponding to the i-th primary sensor module;

[0039] Preset a first risk assessment value threshold F1 and a second risk assessment value threshold F2;

[0040] If fi≤F1, the i-th level sensor module does not generate a warning instruction;

[0041] If F1<fi≤F2, the i-th level 1 sensor module generates a level 1 warning instruction;

[0042] If fi>F2, the i-th first-level sensor module generates a second-level warning instruction.

[0043] In some embodiments of the present application, when the risk assessment value of each primary sensor module is generated in sequence, it includes:

[0044] According to the first-level sensor module sequence K, ki is set as the sensor module to be monitored in sequence;

[0045] Obtaining real-time monitoring data of the sensor module to be monitored at the current feedback time node;

[0046] Generate position data of the sensor module to be monitored at the current feedback time node;

[0047] Generate a first-level call instruction based on the location data;

[0048] Set the target monitoring point according to the first-level call instruction and obtain the image data of the target monitoring point;

[0049] Preprocess the image data and real-time monitoring data of the target monitoring point;

[0050] Generate multiple characteristic indicators according to the preprocessing results;

[0051] Generate a risk assessment value f of the sensor module to be monitored at the current feedback time node based on all characteristic indicators;

[0052] Update the risk assessment value f according to the feedback time node of the sensor module to be monitored;

[0053] Generate the risk assessment value of each primary sensor module in turn.

[0054] In some embodiments of the present application, when generating the risk assessment value f, it includes:

[0055] Generate the primary standard value of each characteristic index;

[0056] Generate secondary standard values ​​of each characteristic index at the current feedback time node according to the expected action trajectory of the sensor module to be monitored;

[0057] Generate a risk assessment value f;

[0058]

[0059] 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; r is the number of characteristic evaluation indicators; gi is the real-time reference value of the i-th characteristic evaluation indicator of the monitored sensor module at the current feedback time node; g1i is the first-level standard value of the i-th characteristic indicator; g2i is the second-level standard value of the i-th characteristic indicator at the current feedback time node.

[0060] In some embodiments of the present application, a method for personnel safety management and control in a power plant area is provided, comprising:

[0061] Preset multiple monitoring cycles, and set the working parameters of the monitoring unit in each monitoring cycle according to the work plan, wherein the monitoring unit includes multiple monitoring modules and multiple sensor modules;

[0062] Establish multiple monitoring points according to equipment parameters in the factory area, and the monitoring points are provided with monitoring modules;

[0063] Establish a personnel management database, generate identity tags and identification tags for each staff member, and establish an identity tag-sensor module mapping table;

[0064] Acquire all image data and all monitoring index data collected by the monitoring unit according to the preset feedback time node, and determine whether to generate an early warning instruction;

[0065] Among them, when multiple monitoring points are established, they include:

[0066] Establish a monitoring point sequence B, B = (b1, b2...bi...bm), where bi is the i-th monitoring point and m is the number of monitoring points.

[0067] In some embodiments of the present application, when the working parameters of the monitoring unit in each monitoring cycle are set according to the working plan, it includes:

[0068] Obtain the work plan within the current monitoring cycle according to the preset monitoring time node;

[0069] Construct a sequence A of personnel waiting to enter the factory, A = (a1, a2...ai...an), where ai is the i-th personnel waiting to enter the factory in the current monitoring period; n is the i-th personnel waiting to enter the factory in the current monitoring period;

[0070] Select multiple primary sensor modules according to the number sequence A of people to enter the factory and the identity tag-sensor module mapping table;

[0071] Establish a primary sensor module sequence K, K = (k1, k2...ki...kn), where ki is the sensor module corresponding to the i-th person to enter the factory in the current monitoring period;

[0072] According to the first-level sensor module sequence K, ki is set as the target first-level sensor module in sequence;

[0073] Generate the expected action trajectory of the target primary sensor module according to the work plan in the current monitoring cycle;

[0074] Obtain the monitoring evaluation value y of the personnel to be admitted to the factory corresponding to the target primary sensor module;

[0075] Set the feedback cycle duration t according to the monitoring evaluation value y;

[0076] Set multiple feedback time nodes of the target primary sensor module according to the expected activity trajectory and feedback cycle duration;

[0077] The target first-level sensor module obtains real-time monitoring data according to the feedback time node;

[0078] Set the feedback time nodes of each primary sensor module in turn;

[0079] Among them, when generating the monitoring evaluation value y, it includes:

[0080]

[0081] Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of historical evaluation indicators; η 1i is the impact factor of the i-th historical evaluation index; c i is the reference value of the i-th historical evaluation index of the personnel to be entered into the factory corresponding to the target primary sensor module; θ2 is the number of trajectory evaluation indicators; η 2i is the influencing factor of the i-th trajectory evaluation index; j i is the reference value of the evaluation index of the i-th trajectory generated based on the expected action trajectory.

[0082] In some embodiments of the present application, when determining whether to generate a warning instruction, the process includes:

[0083] Generate risk assessment values ​​for each primary sensor module in sequence;

[0084] Establish a risk assessment value sequence F, F = (f1, f2...fi...fn), where fi is the risk assessment value corresponding to the i-th primary sensor module;

[0085] Preset a first risk assessment value threshold F1 and a second risk assessment value threshold F2;

[0086] If fi≤F1, the i-th level sensor module does not generate a warning instruction;

[0087] If F1<fi≤F2, the i-th level 1 sensor module generates a level 1 warning instruction;

[0088] If fi>F2, the i-th first-level sensor module generates a second-level warning instruction;

[0089] Wherein, when the risk assessment value of each primary sensor module is generated in sequence, it includes:

[0090] According to the first-level sensor module sequence K, ki is set as the sensor module to be monitored in sequence;

[0091] Obtaining real-time monitoring data of the sensor module to be monitored at the current feedback time node;

[0092] Generate position data of the sensor module to be monitored at the current feedback time node;

[0093] Generate a first-level call instruction based on the location data;

[0094] Set the target monitoring point according to the first-level call instruction and obtain the image data of the target monitoring point;

[0095] Preprocess the image data and real-time monitoring data of the target monitoring point;

[0096] Generate multiple characteristic indicators according to the preprocessing results;

[0097] Generate a risk assessment value f of the sensor module to be monitored at the current feedback time node based on all characteristic indicators;

[0098] Update the risk assessment value f according to the feedback time node of the sensor module to be monitored;

[0099] Generate the risk assessment value of each primary sensor module in turn.

[0100] Compared with the prior art, the personnel safety management system and method for a power plant area in the embodiment of the present application has the following beneficial effects:

[0101] A sensor module worn by personnel is built based on UWB and Beidou positioning technology, so that the position data, pressure data and surrounding smoke concentration data of personnel in the factory can be monitored in real time. Multiple monitoring modules are set up in the factory to collect the posture and movement amplitude data of the staff, so as to identify and warn of violations in time and ensure the safety of the workers in the power plant.

[0102] By establishing a monitoring cycle, corresponding monitoring plans are set for all personnel entering the factory within a single cycle. At the same time, the real-time location data of each person is used to call the corresponding monitoring module to collect personnel posture data, so as to achieve accurate monitoring of individual personnel, reduce the operation and maintenance cost of the monitoring module, and improve its monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 It is a structural diagram of a personnel safety management and control system for a power plant area in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0104] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0105] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0106] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0107] In the description of this application, it should be noted that, unless otherwise clearly specified 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 a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0108] like Figure 1 As shown, a personnel safety management and control system for a power plant area according to a preferred embodiment of the present application includes:

[0109] The central control unit is used to establish multiple monitoring points based on the equipment parameters within the plant;

[0110] A monitoring unit, comprising a plurality of monitoring modules and a plurality of sensor modules;

[0111] The monitoring module is arranged at the monitoring point and is used to collect image data of each monitoring point;

[0112] The sensor module is used to collect monitoring indicator data of each staff member;

[0113] Wherein, a single sensing module includes: a positioning submodule, a pressure submodule and a smoke submodule;

[0114] The central control unit includes:

[0115] The first processing module is used to establish a personnel management database and generate identity tags and identification tags for each staff member;

[0116] The second processing module is used to establish an identity tag-sensing module mapping table;

[0117] The third processing module is used to establish multiple monitoring cycles and set the working parameters of the monitoring unit in each monitoring cycle according to the work plan;

[0118] The fourth processing module is used to obtain all image data and all monitoring indicator data collected by the monitoring unit according to a preset feedback time node, and determine whether to generate an early warning instruction;

[0119] The fifth processing module is used to establish a monitoring point sequence B, B = (b1, b2...bi...bm), where bi is the i-th monitoring point and m is the number of monitoring points.

[0120] Specifically, the monitoring module is preferably a high-resolution camera for collecting data on the posture and movement amplitude of the personnel.

[0121] Specifically, the sensing module is preferably a wearable safety helmet, which is equipped with communication equipment, pressure sensors, smoke sensors and UWB positioning tags. The pressure sensor can monitor whether a person is wearing a safety helmet, the smoke sensor can monitor the smoke concentration around the person, and the UWB positioning tag can generate real-time location data of the person through Beidou positioning technology.

[0122] Specifically, by setting up multiple monitoring points, we can achieve full coverage of monitoring and ensure that the behavior of all operators can be monitored in real time.

[0123] Specifically, by binding the sensor module with the identity tag of the person, accurate monitoring of each person can be achieved. The corresponding identification tag is constructed by collecting the facial data of each person.

[0124] It can be understood that in the above embodiments, a sensor module worn by personnel is constructed based on UWB and Beidou positioning technology, so as to monitor the position data, pressure data and surrounding smoke concentration data of personnel in the factory in real time, and set up multiple monitoring modules in the factory to collect the posture and movement amplitude data of the staff, so as to identify and warn the violations in time and ensure the safety of the workers in the power plant.

[0125] In a preferred embodiment of the present application, the third processing module is also used for:

[0126] Obtain the work plan within the current monitoring cycle according to the preset monitoring time node;

[0127] Construct a sequence A of personnel waiting to enter the factory, A = (a1, a2...ai...an), where ai is the i-th personnel waiting to enter the factory in the current monitoring period; n is the i-th personnel waiting to enter the factory in the current monitoring period;

[0128] Select multiple primary sensor modules according to the number sequence A of people to enter the factory and the identity tag-sensor module mapping table;

[0129] Establish a primary sensor module sequence K, K = (k1, k2...ki...kn), where ki is the sensor module corresponding to the i-th person to enter the factory in the current monitoring period;

[0130] According to the first-level sensor module sequence K, ki is set as the target first-level sensor module in sequence;

[0131] Generate the expected action trajectory of the target primary sensor module according to the work plan in the current monitoring cycle;

[0132] Obtain the monitoring evaluation value y of the personnel to be admitted to the factory corresponding to the target primary sensor module;

[0133] Set the feedback cycle duration t according to the monitoring evaluation value y;

[0134] Set multiple feedback time nodes of the target primary sensor module according to the expected activity trajectory and feedback cycle duration;

[0135] The target first-level sensor module obtains real-time monitoring data according to the feedback time node;

[0136] Set the feedback time nodes of each primary sensor module in turn.

[0137] Specifically, the duration of the monitoring cycle is preferably one working day. The staff who need to enter the factory within a single monitoring cycle are generated through the work plan, thereby constructing a sequence A of people to enter the factory, and the corresponding sensor module is selected as the first-level sensor module through the identity tag to construct a first-level sensor module sequence K.

[0138] Specifically, the corresponding activity range and activity time are generated according to the expected work parameters of a single person to enter the factory, thereby constructing the corresponding expected activity trajectory.

[0139] Specifically, when generating the monitoring evaluation value y, it includes:

[0140]

[0141] Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of historical evaluation indicators; η 1i is the impact factor of the i-th historical evaluation index; c i is the reference value of the i-th historical evaluation index of the personnel to be entered into the factory corresponding to the target primary sensor module; θ2 is the number of trajectory evaluation indicators; η 2i is the influencing factor of the i-th trajectory evaluation index; j iis the reference value of the evaluation index of the i-th trajectory generated based on the expected action trajectory.

[0142] Specifically, by presetting the first fixed coefficient and the second fixed coefficient, all parameters in the model are normalized so that all data in the model are within the same value range.

[0143] Specifically, historical evaluation indicators include but are not limited to the number of violations, violation categories, cumulative violations and other parameters, and trajectory evaluation indicators include but are not limited to the area involved in the trajectory, the number of key areas, the length of time in the factory and other parameters. The larger the monitoring evaluation value, the larger the monitoring evaluation value y, indicating that the corresponding personnel are more likely to violate the regulations, and the greater the safety risk after the violation occurs. The corresponding time interval between adjacent feedback time nodes is also smaller.

[0144] It can be understood that in the above embodiments, a corresponding monitoring plan is set for each person to be shipped out of the factory, so as to achieve accurate monitoring of each staff member, improve monitoring efficiency, and promptly conduct warnings for violations to ensure the safety of the staff.

[0145] In a preferred embodiment of the present application, the fourth processing module is also used for:

[0146] Generate risk assessment values ​​for each primary sensor module in sequence;

[0147] Establish a risk assessment value sequence F, F = (f1, f2...fi...fn), where fi is the risk assessment value corresponding to the i-th primary sensor module;

[0148] Preset a first risk assessment value threshold F1 and a second risk assessment value threshold F2;

[0149] If fi≤F1, the i-th level sensor module does not generate a warning instruction;

[0150] If F1<fi≤F2, the i-th level 1 sensor module generates a level 1 warning instruction;

[0151] If fi>F2, the i-th first-level sensor module generates a second-level warning instruction.

[0152] Specifically, the first-level warning instruction means that the current personnel have violated the regulations. The central control unit will generate a reminder message and send it to the corresponding sensor module to give a voice reminder to the personnel to correct their violations in a timely manner.

[0153] Specifically, the second-level warning instruction means that the current personnel have committed serious violations and need to be cleared out and expelled from the power plant's working area to avoid safety accidents.

[0154] Specifically, when the risk assessment value of each primary sensor module is generated in sequence, it includes:

[0155] According to the first-level sensor module sequence K, ki is set as the sensor module to be monitored in sequence;

[0156] Obtaining real-time monitoring data of the sensor module to be monitored at the current feedback time node;

[0157] Generate position data of the sensor module to be monitored at the current feedback time node;

[0158] Generate a first-level call instruction based on the location data;

[0159] Set the target monitoring point according to the first-level call instruction and obtain the image data of the target monitoring point;

[0160] Preprocess the image data and real-time monitoring data of the target monitoring point;

[0161] Generate multiple characteristic indicators according to the preprocessing results;

[0162] Generate a risk assessment value f of the sensor module to be monitored at the current feedback time node based on all characteristic indicators;

[0163] Update the risk assessment value f according to the feedback time node of the sensor module to be monitored;

[0164] Generate the risk assessment value of each primary sensor module in turn.

[0165] Specifically, according to the real-time location information of personnel, the corresponding monitoring module is called to collect images, thereby improving its monitoring efficiency and reducing the overall operation and maintenance costs of all monitoring modules.

[0166] Specifically, the image data of the target monitoring point is preprocessed, and the corresponding facial data of the person is obtained through the identification tag of the person corresponding to the sensor module to be monitored. Through facial recognition, the person in the image data is quickly locked, and the image data of the target monitoring point is extracted to identify whether there is any violation (such as not wearing a helmet, smoking, not wearing gloves, falling).

[0167] Specifically, when generating the risk assessment value f, it includes:

[0168] Generate the primary standard value of each characteristic index;

[0169] Generate secondary standard values ​​of each characteristic index at the current feedback time node according to the expected action trajectory of the sensor module to be monitored;

[0170] Generate a risk assessment value f;

[0171]

[0172] 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; r is the number of characteristic evaluation indicators; gi is the real-time reference value of the i-th characteristic evaluation indicator of the monitored sensor module at the current feedback time node; g1i is the first-level standard value of the i-th characteristic indicator; g2i is the second-level standard value of the i-th characteristic indicator at the current feedback time node.

[0173] Specifically, all parameters in the model are normalized by presetting the third fixed coefficient and the fourth fixed coefficient, so that each parameter is in the same value range.

[0174] Specifically, the first-level standard value refers to the general standard set based on historical data, and the second-level standard value refers to the fluctuations in the requirements for one or more characteristic indicators due to the influence of environmental parameters in different areas of the power plant. For example, in areas with dust, the minimum smoke concentration can be appropriately increased.

[0175] Specifically, characteristic indicators include but are not limited to the smoke concentration around the person. The higher the concentration, the greater the possibility of illegal smoking. The deviation between the real-time position and the expected position, the deviation between the real-time pressure value and the expected pressure value. The expected pressure value refers to the pressure value generated when the person wears a helmet. The smaller the real-time pressure value, the more irregular the helmet is worn. The deviation of the real-time position, whether there is any violation in the posture, and other parameters.

[0176] Specifically, the larger the risk assessment value, the greater the possibility of safety risks to the current personnel.

[0177] It can be understood that in the above embodiment, by establishing a monitoring cycle, corresponding monitoring plans are set for all personnel entering the factory within a single cycle. At the same time, the real-time location data of each person is used to call the corresponding monitoring module to collect personnel posture data, so as to achieve accurate monitoring of individual personnel, reduce the operation and maintenance cost of the monitoring module, and improve its monitoring efficiency.

[0178] Based on another preferred embodiment of a personnel safety management and control system in a power plant area in any of the above preferred embodiments, this preferred embodiment provides a personnel safety management and control method in a power plant area, including:

[0179] S101: Preset multiple monitoring cycles, and set working parameters of a monitoring unit in each monitoring cycle according to a work plan, wherein the monitoring unit includes multiple monitoring modules and multiple sensor modules;

[0180] S102: Establish multiple monitoring points according to equipment parameters in the factory area, and the monitoring points are provided with monitoring modules;

[0181] S103: Establishing a personnel management database, generating identity tags and identification tags for each staff member, and establishing an identity tag-sensor module mapping table;

[0182] S104: Acquire all image data and all monitoring index data collected by the monitoring unit according to a preset feedback time node, and determine whether to generate an early warning instruction;

[0183] Among them, when multiple monitoring points are established, they include:

[0184] Establish a monitoring point sequence B, B = (b1, b2...bi...bm), where bi is the i-th monitoring point and m is the number of monitoring points.

[0185] In a preferred embodiment of the present application, when the working parameters of the monitoring unit in each monitoring cycle are set according to the working plan, it includes:

[0186] Obtain the work plan within the current monitoring cycle according to the preset monitoring time node;

[0187] Construct a sequence A of personnel waiting to enter the factory, A = (a1, a2...ai...an), where ai is the i-th personnel waiting to enter the factory in the current monitoring period; n is the i-th personnel waiting to enter the factory in the current monitoring period;

[0188] Select multiple primary sensor modules according to the number sequence A of people to enter the factory and the identity tag-sensor module mapping table;

[0189] Establish a primary sensor module sequence K, K = (k1, k2...ki...kn), where ki is the sensor module corresponding to the i-th person to enter the factory in the current monitoring period;

[0190] According to the first-level sensor module sequence K, ki is set as the target first-level sensor module in sequence;

[0191] Generate the expected action trajectory of the target primary sensor module according to the work plan in the current monitoring cycle;

[0192] Obtain the monitoring evaluation value y of the personnel to be admitted to the factory corresponding to the target primary sensor module;

[0193] Set the feedback cycle duration t according to the monitoring evaluation value y;

[0194] Set multiple feedback time nodes of the target primary sensor module according to the expected activity trajectory and feedback cycle duration;

[0195] The target first-level sensor module obtains real-time monitoring data according to the feedback time node;

[0196] Set the feedback time nodes of each primary sensor module in turn;

[0197] Among them, when generating the monitoring evaluation value y, it includes:

[0198]

[0199] Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of historical evaluation indicators; η 1i is the impact factor of the i-th historical evaluation index; c i is the reference value of the i-th historical evaluation index of the personnel to be entered into the factory corresponding to the target primary sensor module; θ2 is the number of trajectory evaluation indicators; η 2i is the influencing factor of the i-th trajectory evaluation index; j i is the reference value of the evaluation index of the i-th trajectory generated based on the expected action trajectory.

[0200] In a preferred embodiment of the present application, when determining whether to generate a warning instruction, the process includes:

[0201] Generate risk assessment values ​​for each primary sensor module in sequence;

[0202] Establish a risk assessment value sequence F, F = (f1, f2...fi...fn), where fi is the risk assessment value corresponding to the i-th primary sensor module;

[0203] Preset a first risk assessment value threshold F1 and a second risk assessment value threshold F2;

[0204] If fi≤F1, the i-th level sensor module does not generate a warning instruction;

[0205] If F1<fi≤F2, the i-th level 1 sensor module generates a level 1 warning instruction;

[0206] If fi>F2, the i-th first-level sensor module generates a second-level warning instruction;

[0207] Wherein, when the risk assessment value of each primary sensor module is generated in sequence, it includes:

[0208] According to the first-level sensor module sequence K, ki is set as the sensor module to be monitored in sequence;

[0209] Obtaining real-time monitoring data of the sensor module to be monitored at the current feedback time node;

[0210] Generate position data of the sensor module to be monitored at the current feedback time node;

[0211] Generate a first-level call instruction based on the location data;

[0212] Set the target monitoring point according to the first-level call instruction and obtain the image data of the target monitoring point;

[0213] Preprocess the image data and real-time monitoring data of the target monitoring point;

[0214] Generate multiple characteristic indicators according to the preprocessing results;

[0215] Generate a risk assessment value f of the sensor module to be monitored at the current feedback time node based on all characteristic indicators;

[0216] Update the risk assessment value f according to the feedback time node of the sensor module to be monitored;

[0217] Generate the risk assessment value of each primary sensor module in turn.

[0218] According to the first concept of this application, a sensor module worn by personnel is constructed based on UWB and Beidou positioning technology, so as to carry out real-time monitoring of the position data, pressure data and surrounding smoke concentration data of personnel in the factory area, and set up multiple monitoring modules in the factory area to collect the posture and movement amplitude data of the staff, so as to identify and warn the violations in time and ensure the safety of the workers in the power plant.

[0219] According to the second concept of the present application, by establishing a monitoring cycle, corresponding monitoring plans are set for all personnel entering the factory within a single cycle. At the same time, the real-time location data of each person is used to call the corresponding monitoring module to collect personnel posture data, so as to achieve accurate monitoring of individual personnel, reduce the operation and maintenance cost of the monitoring module, and improve its monitoring efficiency.

[0220] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A personnel safety management and control system for a power plant area, characterized in that: include: The central control unit is used to establish multiple monitoring points based on the equipment parameters within the plant; A monitoring unit, comprising a plurality of monitoring modules and a plurality of sensor modules; The monitoring module is arranged at the monitoring point and is used to collect image data of each monitoring point; The sensor module is used to collect monitoring index data of each staff member; Wherein, a single sensing module includes: a positioning submodule, a pressure submodule and a smoke submodule; The central control unit comprises: The first processing module is used to establish a personnel management database and generate identity tags and identification tags for each staff member; The second processing module is used to establish an identity tag-sensing module mapping table; The third processing module is used to establish multiple monitoring cycles and set the working parameters of the monitoring unit in each monitoring cycle according to the work plan; The fourth processing module is used to obtain all image data and all monitoring indicator data collected by the monitoring unit according to a preset feedback time node, and determine whether to generate an early warning instruction; The fifth processing module is used to establish a monitoring point sequence B, B = (b1, b2...bi...bm), where bi is the i-th monitoring point and m is the number of monitoring points.

2. The personnel safety management and control system for a power plant area according to claim 1, characterized in that: The third processing module is also used for: Obtain the work plan within the current monitoring cycle according to the preset monitoring time node; Construct a sequence A of personnel waiting to enter the factory, A = (a1, a2...ai...an), where ai is the i-th personnel waiting to enter the factory in the current monitoring period; n is the i-th personnel waiting to enter the factory in the current monitoring period; Select multiple primary sensor modules according to the number sequence A of people to enter the factory and the identity tag-sensor module mapping table; Establish a primary sensor module sequence K, K = (k1, k2...ki...kn), where ki is the sensor module corresponding to the i-th person to enter the factory in the current monitoring period; Set the feedback time nodes of each primary sensor module in turn.

3. The personnel safety management and control system for a power plant area according to claim 2, characterized in that: When setting the feedback time nodes of each primary sensor module in sequence, it includes: According to the first-level sensor module sequence K, ki is set as the target first-level sensor module in sequence; Generate the expected action trajectory of the target primary sensor module according to the work plan in the current monitoring cycle; Obtain the monitoring evaluation value y of the personnel to be admitted to the factory corresponding to the target primary sensor module; Set the feedback cycle duration t according to the monitoring evaluation value y; Set multiple feedback time nodes of the target primary sensor module according to the expected activity trajectory and feedback cycle duration; The target first-level sensor module obtains real-time monitoring data according to the feedback time node; Set the feedback time nodes of each primary sensor module in turn.

4. The personnel safety management and control system for a power plant area according to claim 3, characterized in that: When generating the monitoring evaluation value y, it includes: Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of historical evaluation indicators; η 1i is the impact factor of the i-th historical evaluation index; c i is the reference value of the i-th historical evaluation index of the personnel to be entered into the factory corresponding to the target primary sensor module; θ2 is the number of trajectory evaluation indexes; η 2i is the influencing factor of the i-th trajectory evaluation index; j i is the reference value of the evaluation index of the i-th trajectory generated based on the expected action trajectory.

5. The personnel safety management and control system for a power plant area according to claim 3, characterized in that: The fourth processing module is further used for: Generate risk assessment values ​​for each primary sensor module in sequence; Establish a risk assessment value sequence F, F = (f1, f2...fi...fn), where fi is the risk assessment value corresponding to the i-th primary sensor module; Preset a first risk assessment value threshold F1 and a second risk assessment value threshold F2; If fi≤F1, the i-th level sensor module does not generate a warning instruction; If F1<fi≤F2, the i-th level 1 sensor module generates a level 1 warning instruction; If fi>F2, the i-th first-level sensor module generates a second-level warning instruction.

6. The personnel safety control system for a power plant area according to claim 5, characterized in that: When generating the risk assessment value of each primary sensor module in sequence, it includes: According to the first-level sensor module sequence K, ki is set as the sensor module to be monitored in sequence; Obtaining real-time monitoring data of the sensor module to be monitored at the current feedback time node; Generate position data of the sensor module to be monitored at the current feedback time node; Generate a first-level call instruction based on the location data; Set the target monitoring point according to the first-level call instruction and obtain the image data of the target monitoring point; Preprocess the image data and real-time monitoring data of the target monitoring point; Generate multiple characteristic indicators according to the preprocessing results; Generate a risk assessment value f of the sensor module to be monitored at the current feedback time node based on all characteristic indicators; Update the risk assessment value f according to the feedback time node of the sensor module to be monitored; Generate the risk assessment value of each primary sensor module in turn.

7. The personnel safety management and control system for a power plant area according to claim 6, characterized in that: When generating the risk assessment value f, it includes: Generate the primary standard value of each characteristic index; Generate secondary standard values ​​of each characteristic index at the current feedback time node according to the expected action trajectory of the sensor module to be monitored; Generate a risk assessment value f; 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; r is the number of characteristic evaluation indicators; gi is the real-time reference value of the i-th characteristic evaluation indicator of the monitored sensor module at the current feedback time node; g1i is the first-level standard value of the i-th characteristic indicator; g2i is the second-level standard value of the i-th characteristic indicator at the current feedback time node.

8. A personnel safety control method for a power plant area, applied to the personnel safety control system for a power plant area as claimed in any one of claims 1 to 7, characterized in that: include: Preset multiple monitoring cycles, and set the working parameters of the monitoring unit in each monitoring cycle according to the work plan, wherein the monitoring unit includes multiple monitoring modules and multiple sensor modules; Establish multiple monitoring points according to equipment parameters in the factory area, and the monitoring points are provided with monitoring modules; Establish a personnel management database, generate identity tags and identification tags for each staff member, and establish an identity tag-sensor module mapping table; Acquire all image data and all monitoring index data collected by the monitoring unit according to the preset feedback time node, and determine whether to generate an early warning instruction; Among them, when multiple monitoring points are established, they include: Establish a monitoring point sequence B, B = (b1, b2...bi...bm), where bi is the i-th monitoring point and m is the number of monitoring points.

9. The method for personnel safety control in a power plant area according to claim 8, characterized in that: When setting the working parameters of the monitoring unit in each monitoring cycle according to the work plan, it includes: Obtain the work plan within the current monitoring cycle according to the preset monitoring time node; Construct a sequence A of personnel waiting to enter the factory, A = (a1, a2...ai...an), where ai is the i-th personnel waiting to enter the factory in the current monitoring period; n is the i-th personnel waiting to enter the factory in the current monitoring period; Select multiple primary sensor modules according to the number sequence A of people to enter the factory and the identity tag-sensor module mapping table; Establish a primary sensor module sequence K, K = (k1, k2...ki...kn), where ki is the sensor module corresponding to the i-th person to enter the factory in the current monitoring period; According to the first-level sensor module sequence K, ki is set as the target first-level sensor module in sequence; Generate the expected action trajectory of the target primary sensor module according to the work plan in the current monitoring cycle; Obtain the monitoring evaluation value y of the personnel to be admitted to the factory corresponding to the target primary sensor module; Set the feedback cycle duration t according to the monitoring evaluation value y; Set multiple feedback time nodes of the target primary sensor module according to the expected activity trajectory and feedback cycle duration; The target first-level sensor module obtains real-time monitoring data according to the feedback time node; Set the feedback time nodes of each primary sensor module in sequence; Among them, when generating the monitoring evaluation value y, it includes: Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of historical evaluation indicators; η 1i is the impact factor of the i-th historical evaluation index; c i is the reference value of the i-th historical evaluation index of the personnel to be entered into the factory corresponding to the target primary sensor module; θ2 is the number of trajectory evaluation indexes; η 2i is the influencing factor of the i-th trajectory evaluation index; j i is the reference value of the evaluation index of the i-th trajectory generated based on the expected action trajectory.

10. The method for personnel safety control in a power plant area according to claim 8, characterized in that: When judging whether to generate an early warning instruction, it includes: Generate risk assessment values ​​for each primary sensor module in sequence; Establish a risk assessment value sequence F, F = (f1, f2...fi...fn), where fi is the risk assessment value corresponding to the i-th primary sensor module; Preset a first risk assessment value threshold F1 and a second risk assessment value threshold F2; If fi≤F1, the i-th level sensor module does not generate a warning instruction; If F1<fi≤F2, the i-th level 1 sensor module generates a level 1 warning instruction; If fi>F2, the i-th first-level sensor module generates a second-level warning instruction; Wherein, when the risk assessment value of each primary sensor module is generated in sequence, it includes: According to the first-level sensor module sequence K, ki is set as the sensor module to be monitored in sequence; Obtaining real-time monitoring data of the sensor module to be monitored at the current feedback time node; Generate position data of the sensor module to be monitored at the current feedback time node; Generate a first-level call instruction based on the location data; Set the target monitoring point according to the first-level call instruction and obtain the image data of the target monitoring point; Preprocess the image data and real-time monitoring data of the target monitoring point; Generate multiple characteristic indicators according to the preprocessing results; Generate a risk assessment value f of the sensor module to be monitored at the current feedback time node based on all characteristic indicators; Update the risk assessment value f according to the feedback time node of the sensor module to be monitored; Generate the risk assessment value of each primary sensor module in turn.

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