Safety production video monitoring system and method

Abstract

The invention relates to the technical field of safety monitoring systems, and discloses a safety production video monitoring system and method, and the system comprises a monitoring unit, a data processing unit, and an early warning unit. The monitoring unit comprises an equipment module, a mobile device module and a recording module and is used for acquiring data information to form data points and transmitting the data points to the data processing unit; the data processing unit comprises a data judgment module, a data analysis module and a data calculation module; the early warning unit comprises a display module, a linkage module and an emergency module. By means of real-time monitoring, data analysis, early warning and the like, comprehensiveness and effectiveness of monitoring are guaranteed, data processing amount is effectively reduced, data processing efficiency is improved, early prediction and timely processing are achieved, potential risks are effectively early warned and prevented, comprehensive, timely and accurate video supervision is achieved, safety in the production process is guaranteed, and production efficiency is improved. The production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of video monitoring systems, and in particular to a production safety video monitoring system and method. Background Art

[0002] In the complex environment of power production, safety monitoring is the key to ensuring stable operation and avoiding potential risks. However, traditional video monitoring methods have obvious limitations and cannot meet the strict requirements of modern power production for safety monitoring. In actual production operations, existing video monitoring systems often have incomplete coverage, have monitoring blind spots, and cannot effectively monitor key areas. As a result, some potential safety hazards may not be discovered in time during the power production process, thereby increasing the possibility of accidents.

[0003] In addition, even if the monitoring system can capture certain abnormal behaviors or equipment failures, due to the lack of intelligent analysis and early warning mechanisms, the system is often unable to quickly and accurately judge and take effective application measures. This lag not only prolongs the time for accident handling, but may also expand the scope of the accident and cause more serious consequences. This makes power production subject to potential risks, posing a threat to personnel safety, equipment safety, and the stability of power supply.

[0004] Therefore, there is an urgent need to develop a video surveillance system that can realize comprehensive and real-time monitoring of the power production process, respond to various emergencies in a timely and accurate manner, and ensure the safe and stable operation of power production. Summary of the invention

[0005] The present invention aims to provide a safe production video monitoring system and method to solve the problem that the existing video monitoring system has incomplete monitoring, cannot timely discover and quickly handle various emergencies, and leads to potential risks in power production.

[0006] To achieve the above object, the present invention adopts the following technical solution: a production safety video monitoring system, comprising a monitoring unit, a data processing unit and an early warning unit; the monitoring unit comprises an equipment module, a mobile device module and a recording module, which are used to collect data information to form data points and transmit them to the data processing unit;

[0007] The data processing unit includes a data determination module, a data analysis module and a data inference module; the data determination module is used to classify the collected data information according to the set data type, and the data type includes traditional data, emerging data and change data; the data analysis module is used to determine the data fluctuation state according to the data type, and filter out relevant data according to the fluctuation state, and mark them as fluctuation value and impact value respectively; the data inference module is used to determine the impact coefficient of the marked data according to the historical data, and determine the monitoring result according to the impact coefficient level; and transmit the monitoring result to the early warning unit;

[0008] The early warning unit includes a display module, a linkage module and an emergency module; the display module is used to display the monitoring results in a set manner; the linkage module is used to send instruction information to the corresponding monitoring unit according to the display content and input content; the emergency module is used to initiate necessary emergency measures according to the instruction information.

[0009] Furthermore, the data information includes data content, data features, geographic location and corresponding timestamp; and the acquired data points are stored accordingly to form a data set in order of timestamps.

[0010] Furthermore, the traditional data is defined as data that has existed in historical data for more than 5 years and has a change rate of less than 20%; the emerging data is defined as new types of data that have appeared for less than 3 years; and the variable data is defined as data with a change rate greater than 20% within a certain period.

[0011] Furthermore, the fluctuation state includes normal fluctuation, small fluctuation, large fluctuation and abnormal fluctuation; the data correlation coefficient is calculated according to the fluctuation state, and the data with the correlation coefficient greater than the set threshold is screened out as relevant data.

[0012] Furthermore, the equipment module includes a monitoring camera, the mobile device module includes a smart helmet and a handheld detection device, and the recording module includes a work recorder, a sensor, and a data storage device.

[0013] Furthermore, the display module includes a map display section, a statistical display section and a distribution switching display section; the map display section is used to display the monitoring results in the form of a map, the statistical display section is used to integrate the monitoring results and display them in the form of set data, and the distribution switching display section is used to switch or enlarge multiple monitoring screens.

[0014] Furthermore, the linkage module includes a call section, a dispatch section and a control section; the call section is used to transmit real-time information to personnel in the target area through a voice communication function; the dispatch section is used to input dispatch instructions according to monitoring results; and the control section is used to self-start or input corresponding control instructions according to monitoring results.

[0015] At the same time, the present solution also provides a production safety video monitoring method, which is applied to the above-mentioned monitoring system and includes the following steps:

[0016] Step 1: Classify historical data according to data type to form traditional data, emerging data and change data;

[0017] Step 2: Acquire real-time data information to form a data set; classify the data set according to data type;

[0018] Step 3, analyzing the fluctuation state of the data according to the data type and determining the degree of data fluctuation;

[0019] Step 4, obtaining relevant data according to the fluctuation state, and analyzing the influence coefficient of the current fluctuation state on the relevant data;

[0020] Step 5: Calculate the monitoring results based on the impact coefficient and display the monitoring results and real-time data in a visual form.

[0021] Furthermore, in step 2, the data acquisition source is set according to the station location and the spatial distribution within the station, and real-time data is acquired according to the set acquisition frequency.

[0022] Furthermore, in step 4, the related variables with a higher degree of relationship are screened in the small-amplitude fluctuation data; the related variable data with a medium degree of relationship or above are screened in the large-amplitude fluctuation data; and the related variables with a certain degree of relationship are screened in the abnormal fluctuation data.

[0023] The principles and advantages of this solution are:

[0024] This solution deploys corresponding monitoring equipment in key areas to ensure that each site can achieve extensive and accurate monitoring coverage, while streamlining the amount of data and enhancing the stability of the monitoring system. The collected data is classified and processed according to its data type to improve data processing speed and reduce analysis delays. Furthermore, targeted fluctuation trend analysis is carried out for different types of data, and based on these fluctuations, the possible anomalies or risks are evaluated, thereby achieving early prediction and assessment of risks. This process can assist regulators in gaining insight into risks in advance, making it easier to take preventive measures in advance or to respond quickly to identified or potential risks, ultimately achieving comprehensive coverage, instant feedback and accuracy of video surveillance, enhancing regulatory effectiveness, ensuring the stability and safety of production processes, and effectively reducing the probability of potential risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a structural schematic diagram of a production safety video monitoring system.

[0026] Figure 2 The present invention is a flowchart of a production safety video monitoring method. DETAILED DESCRIPTION

[0027] The following is further described in detail through specific implementation methods:

[0028] Embodiment 1

[0029] This embodiment is shown in the attached Figure 1 As shown, a safe production video monitoring system ensures safety in the production process through real-time monitoring, data analysis and early warning means. At the same time, by classifying and processing the acquired data, the effective acquisition of necessary data is ensured, while the amount of data is reduced and the efficiency of data analysis is improved. And according to the data type, analysis and calculation are performed to improve the accuracy and reliability of the monitoring results, and the effect of early prediction is achieved, so that the emergency can be responded to and handled quickly, the risk factors are reduced, and the operation safety is improved. In this embodiment, the video monitoring system includes a monitoring unit, a data processing unit and an early warning unit.

[0030] In this embodiment, the monitoring unit includes an equipment module, a mobile device module and a recording module, which are used to collect data information to form data points and transmit them to the data processing unit.

[0031] The monitoring unit is installed in each site and worn by the operator, or carried by the operator. It collects dynamic data from various intelligent devices to form data points, including daily inspection line data, face recognition data, IoT device signals, production status and other data information, providing basic data for the entire system, and transmitting and storing data through wireless and wired networks to ensure real-time, accurate, continuous and intelligent data acquisition.

[0032] In this embodiment, the equipment module includes monitoring equipment installed in key areas of the station, where key areas include core components in the power generation process of the power plant, areas that require special approval to enter, or areas that require wearing specific protective equipment, areas with dense equipment such as major power generation equipment, etc., which are divided based on importance and safety as indicators to ensure comprehensive supervision and data acquisition of key areas. The monitoring equipment is mainly image data acquisition equipment such as installed high-definition cameras.

[0033] The mobile device module includes smart helmets and handheld detection equipment, such as protective helmets and protective clothing with positioning functions, which are used to obtain basic information of operators, behavioral data, inspection routes and other data information. The recording module includes work recorders, sensors, data storage devices, etc., which are used to obtain historical data such as employee scheduling, on-the-job status, inspection arrangements, production status data, and maintenance processing.

[0034] In this embodiment, the collected data information includes data content, data features, geographic location and corresponding timestamp, wherein the data content may include parameters such as data source, type, format, data volume, etc. according to the set collection point, and the data features include types such as digital type, character type, etc., data distribution characteristics, and the degree of relationship between related variables. In this embodiment, the degree of relationship between related variables is divided into higher level, medium level and low level according to direct influence and indirect influence. The geographic location is the geographic location where the data is generated, including longitude and latitude, etc., and the timestamp is the time information of data acquisition. The acquired data points are stored correspondingly to form a data set in order of timestamps.

[0035] The data processing unit includes a data determination module, a data analysis module and a data inference module.

[0036] In this embodiment, the data determination module is used to classify the collected data information according to the set data type, and the data type includes traditional data, emerging data and changed data.

[0037] Among them, traditional data is defined as data that exists in historical data for more than 5 years and has a change rate of less than 20%. Emerging data is defined as new types of data that have appeared for less than 3 years. Changing data is defined as data with a change rate of more than 20% within a certain period.

[0038] In this embodiment, the stored historical data is used as a data set, and for each data item, the length of time from its first appearance to the current time is calculated. And for each data item, its rate of change within a certain period of time is calculated. Its rate of change can determine the change in its name definition based on the source of the data item, and the number of name changes is counted every quarter. The rate of change is calculated using (current value-initial value) / initial value*100%. When the rate of change is less than 20% and the duration is greater than 5 years, it is traditional data. When the rate of change is greater than 20%, it is variable data. When the duration is less than 3 years, it is emerging data. For data samples less than 3 years, there is no need to calculate the rate of change, and it is directly classified as emerging data. At the same time, the rate of change is updated according to the set time period to update the data type and improve the accuracy of data classification.

[0039] The data analysis module is used to determine the data fluctuation state according to the data type, and filter out relevant data according to the fluctuation state, marking them as fluctuation values ​​and impact values ​​respectively.

[0040] In this embodiment, the fluctuation state includes normal fluctuation, small fluctuation, large fluctuation and abnormal fluctuation; the data correlation coefficient is calculated according to the fluctuation state, and the data with a correlation coefficient greater than a set threshold is screened out as relevant data.

[0041] In this embodiment, the normal fluctuation is the data change fluctuation under normal production conditions.

[0042] Small fluctuations are slight changes in data within a relatively stable range over a period of time. That is, the data values ​​fluctuate slightly around a central level or average value. This fluctuation usually does not deviate significantly from the central level, but remains within a relatively stable range. Fluctuations may be caused by a variety of random factors, such as measurement errors, slight changes in environmental conditions, and slight differences in equipment operation. This central level can be regarded as the long-term trend or stable state of the data.

[0043] Large fluctuations refer to the fluctuation of data in a large discrete range over a period of time. In a certain period of time, the data values ​​deviate significantly from their central level or average value, showing a large degree of dispersion. This kind of fluctuation usually means that there is some unstable factor or multiple complex factors behind the data.

[0044] Abnormal fluctuations are unexpected changes in data within a short period of time. Data values ​​change dramatically in a short period of time, far exceeding their long-term average level or normal fluctuation range. Abnormal fluctuations may be caused by a variety of factors, including changes in the external environment, internal problems, data collection and processing errors, etc.

[0045] In this embodiment, the moving average of the time series data is first calculated to smooth the data and identify trends, and the moving standard deviation of the time series data is calculated to measure the degree of data fluctuation. When the standard deviation of the data is within the preset normal range, it is a normal fluctuation state, when the standard deviation is lower than the threshold of normal fluctuation, it is a small fluctuation, when the standard deviation is higher than the threshold of normal fluctuation, it is a large fluctuation, and when the data point deviates significantly from the moving average, it is an abnormal fluctuation.

[0046] According to the fluctuation state of the monitored data, the relevant data is screened out according to the relationship level and marked as the impact value. For example, in the data with small fluctuations, according to the currently marked fluctuation value and the relationship level between the relevant variables, the relevant variable data with a higher relationship level is screened out, that is, the relevant variables that may also bring abnormal effects in small fluctuations are marked as impact values. In the data with large fluctuations, the relevant variable data with a medium or higher relationship level is screened out and marked as the impact value. In the data with abnormal fluctuations, the abnormal values ​​are marked and the relevant variables are marked as impact values.

[0047] The data inference module is used to determine the influence coefficient of the marked data based on historical data, determine the monitoring results according to the level of the influence coefficient; and transmit the monitoring results to the early warning unit.

[0048] In this embodiment, in combination with the maintenance records in the historical data, the fault or risk is associated with the specific cause and marked to form a relationship chain, and the impact measurement index of the change of the factor on other factors in the relationship chain is calculated, which is the impact coefficient of the current factor and the related variable. The impact level is divided according to the impact coefficient, and different forms of display and reminder are performed according to the impact level. In this embodiment, the high impact level is highlighted, and the relevant control instructions are started accordingly to reduce risks and losses. Early warning prompts are given for the medium impact level; attention prompts are given for the low impact level. So that the monitoring personnel can take corresponding measures in time according to the prompt status, and can predict potential risks, thereby reducing the occurrence of risks, improving supervision efficiency, and ensuring safe operation.

[0049] The early warning unit includes a display module, a linkage module and an emergency module.

[0050] In this embodiment, the display module is used to display the monitoring results in a set manner. The display module includes a map display panel, a statistical display panel, and a distribution switching display panel. The map display panel is used to display the monitoring results in the form of a map, and to display the location and operating status of each station equipment in a map mode. Through the equipment configuration or the location information (longitude, latitude) actively reported by the equipment to the system, the location of each station where the equipment is located can be intuitively displayed on the map, so as to intuitively display the location and operating status of the stations in each region, and can quickly understand the specific direction and geographical location when issuing an early warning message, so as to determine the current tense urgency based on the on-site environment, weather conditions, etc., thereby improving processing efficiency.

[0051] The statistical display section is used to integrate the monitoring results and display them in the set data format, such as the total power generation of each station, operating hours, number of people on duty, number of inspections, number of maintenance, alarm records and other overall data. It can also display the query, integration and display of frame extraction data of each station according to the selection, so as to intuitively understand the trends and key indicators of the data, realize the effect of rapid query and overall management, and improve the comprehensiveness and accuracy of overall monitoring.

[0052] The distribution switching display panel is used to switch or enlarge multiple monitoring screens. By switching between stations, all station equipment connected to the system can be monitored in real time and the operating status of the equipment can be mastered. In this embodiment, the production display screen used supports the selection and switching of multiple video input signals, and displays the images from different monitoring devices in real time to ensure that key information is displayed first.

[0053] The linkage module is used to send command information to the corresponding monitoring unit according to the display content and input content. The linkage module includes a call section, a dispatch section and a control section. The call section is used to transmit real-time information to personnel in the target area through the voice communication function. In this embodiment, through the monitoring content, when violations or risky behaviors are found on the scene, one-to-many one-way shouting commands or two-way one-to-one communication can be directly implemented through the call section to quickly issue emergency instructions or reminders, so that the target population can immediately take corresponding actions to reduce disaster losses and ensure personnel safety, realize automated and remote operations, and effectively improve the timeliness and accuracy of information transmission, thereby reducing labor costs and improving response efficiency.

[0054] The dispatching section is used to input dispatching instructions according to the monitoring results, including control start and stop instructions, material coordination and dispatching instructions, etc., so as to quickly respond to emergencies, stop losses in time, reduce risks or dangers, and improve safety. The control section is used to automatically start the system or input corresponding control instructions according to the monitoring results to achieve intelligent control and improve monitoring efficiency.

[0055] The emergency module is used to initiate necessary emergency measures according to the instruction information.

[0056] In this embodiment, the key area is set as the data source, and the corresponding monitoring equipment is set to ensure that each station can achieve comprehensive and effective monitoring, while reducing the amount of data and improving the stability of monitoring. The acquired data is classified according to the data type and processed separately according to the data type to improve the data processing efficiency and reduce the delay of data analysis. At the same time, the fluctuation state is analyzed separately according to the data type, and the abnormal impact or risk impact that may be brought about is determined in combination with the fluctuation state, so as to achieve early prediction and prejudgment, assist supervisors to discover risks in advance, and take early prevention or more timely response measures to risks or potential risks, so as to achieve comprehensive, timely and accurate video supervision, improve the effectiveness of supervision, ensure the stability and safety of production, and thus reduce potential risks.

[0057] Embodiment 2

[0058] In this embodiment, a safe production video monitoring method is provided, which is applied to the above-mentioned video monitoring system. Figure 2 As shown, the following steps are included:

[0059] S1, classify historical data according to data type to form traditional data, emerging data and changing data.

[0060] S2, obtain real-time data information to form a data set; classify the data set according to data type.

[0061] In this embodiment, a key area is defined according to the station location and the spatial distribution within the station, and is set as a data acquisition source, so that data acquisition equipment is installed and real-time data is acquired according to the set acquisition frequency. The acquired real-time data is classified according to the data type.

[0062] S3, analyzing the fluctuation state of the data according to the data type, and determining the degree of fluctuation of the data.

[0063] In this embodiment, the fluctuation state includes normal fluctuation, small fluctuation, large fluctuation and abnormal fluctuation. In each data type, the moving average and standard deviation of its time series data are calculated using historical data, and this is used as the analysis standard to measure the moving trend and fluctuation degree of real-time data. When the standard deviation of the data is within the preset normal range, it is a normal fluctuation state. When the standard deviation is lower than the threshold of normal fluctuation, it is a small fluctuation. When the standard deviation is higher than the threshold of normal fluctuation, it is a large fluctuation. When the data point deviates significantly from the moving average, it is an abnormal fluctuation.

[0064] S4, obtaining relevant data according to the degree of fluctuation state, and analyzing the influence coefficient of the current fluctuation state on the relevant data.

[0065] In this embodiment, related variables with a higher degree of relationship are screened in small-amplitude fluctuation data; related variable data with a medium or above degree of relationship are screened in large-amplitude fluctuation data; related variables with a degree of relationship are screened in abnormal fluctuation data, marked as influence values, and their influence coefficients are calculated based on the influence values.

[0066] S5, calculates the monitoring results based on the impact coefficient, and displays the monitoring results and real-time data in a visual form.

[0067] In this embodiment, the safety of the enterprise's production process is ensured and production efficiency is improved through real-time monitoring, data analysis, early warning and other means. By installing high-definition cameras in key areas of the station, the safe production video supervision system can monitor the station in real time, timely discover and warn unsafe behaviors, and improve the efficiency and accuracy of safety management.

[0068] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A production safety video monitoring system, characterized by: It includes a monitoring unit, a data processing unit and an early warning unit; the monitoring unit includes an equipment module, a mobile device module and a recording module, which are used to collect data information to form data points and transmit them to the data processing unit; The data processing unit includes a data determination module, a data analysis module and a data inference module; the data determination module is used to classify the collected data information according to the set data type, and the data type includes traditional data, emerging data and change data; the data analysis module is used to determine the data fluctuation state according to the data type, and filter out relevant data according to the fluctuation state, and mark them as fluctuation value and impact value respectively; the data inference module is used to determine the impact coefficient of the marked data according to the historical data, and determine the monitoring result according to the impact coefficient level; and transmit the monitoring result to the early warning unit; The early warning unit includes a display module, a linkage module and an emergency module; the display module is used to display the monitoring results in a set manner; the linkage module is used to send instruction information to the corresponding monitoring unit according to the display content and input content; the emergency module is used to initiate necessary emergency measures according to the instruction information.

2. A production safety video monitoring system according to claim 1, characterized in that: The data information includes data content, data features, geographic location and corresponding timestamps; and the acquired data points are stored accordingly to form a data set in order of timestamps.

3. A production safety video monitoring system according to claim 1, characterized in that: The traditional data is defined as data that has existed in historical data for more than 5 years and has a change rate of less than 20%; the emerging data is defined as new types of data that have appeared for less than 3 years; and the variable data is defined as data with a change rate greater than 20% within a certain period.

4. A production safety video monitoring system according to claim 1, characterized in that: The fluctuation states include normal fluctuations, small fluctuations, large fluctuations and abnormal fluctuations; according to the fluctuation states and the set relationship level, relevant data are screened out and marked as impact values.

5. A production safety video monitoring system according to claim 1, characterized in that: The equipment module includes a monitoring camera, the mobile device module includes an intelligent helmet and a handheld detection device, and the recording module includes a work recorder, a sensor, and a data storage device.

6. A production safety video monitoring system according to claim 1, characterized in that: The display module includes a map display section, a statistical display section and a distribution switching display section; the map display section is used to display the monitoring results in the form of a map, the statistical display section is used to integrate the monitoring results and display them in the form of set data, and the distribution switching display section is used to switch or enlarge multiple monitoring screens.

7. A production safety video monitoring system according to claim 1, characterized in that: The linkage module includes a call panel, a dispatch panel and a control panel; the call panel is used to transmit real-time information to people in the target area through voice communication function; The dispatching module is used to input dispatching instructions according to the monitoring results; the control module is used to self-start or input corresponding control instructions according to the monitoring results.

8. A production safety video monitoring method, characterized in that: A production safety video monitoring system applied to any one of claims 1 to 7, comprising the following steps: Step 1: Classify historical data according to data type to form traditional data, emerging data and change data; Step 2: Acquire real-time data information to form a data set; classify the data set according to data type; Step 3, analyzing the fluctuation state of the data according to the data type and determining the degree of data fluctuation; Step 4, obtaining relevant data according to the fluctuation state, and analyzing the influence coefficient of the current fluctuation state on the relevant data; Step 5: Calculate the monitoring results based on the impact coefficient and display the monitoring results and real-time data in a visual form.

9. A production safety video monitoring method according to claim 8, characterized in that: In step 2, the data acquisition source is set according to the station location and the spatial distribution within the station, and real-time data is acquired according to the set acquisition frequency.

10. A production safety video monitoring method according to claim 8, characterized in that: In step 4, select the relevant variables with a higher relationship level in the small fluctuation data; select the relevant variable data with a medium or higher relationship level in the large fluctuation data; Screening out relevant variables with a degree of relationship in abnormally volatile data.

Images