Method and device for regional grid-based dynamic risk classification control of metal smelting enterprises

Abstract

The present invention relates to the technical field of risk management and control, and provides a method and device for regional grid-based dynamic risk classification management and control of metal smelting enterprises. The method comprises: the central service platform compares the sensor data with the corresponding monitoring threshold to determine whether the sensor data is abnormal; first, for each grid, an alarm analysis is performed on the abnormal sensor data in the corresponding grid to obtain the alarm of each grid, and then the alarm of the downstream grid is filtered out in combination with the alarm of the upstream grid to obtain the final alarm analysis result. The present invention divides the grid, first performs an alarm analysis on each grid, and then filters the alarm of the downstream grid in combination with the alarm of the upstream grid, thereby effectively removing the number of duplicate alarms, determining the grid where the actual alarm is located, and thus assigning responsibility to the specific grid, making it easier for grid managers to handle alarms.

Description

Technical Field

[0001] The present invention relates to the field of risk management technology, and in particular to a method and device for regional grid-based dynamic risk classification management and control in a metal smelting enterprise. Background Art

[0002] With the continuous development of science and technology, people have also put forward new demands for the monitoring and control of safety risks. They require that in addition to conventional control measures, online monitoring should be provided, and the data generated by monitoring should be integrated and analyzed, so as to timely grasp the real-time status of production safety risks.

[0003] Metal smelting enterprises usually occupy a large area and have a large number of equipment, most of which are heavy industrial equipment. Therefore, people often have higher requirements for risk monitoring in metal smelting enterprises. In the existing technology, risk monitoring in metal smelting enterprises still has the following shortcomings:

[0004] (1) The existing security information still has gaps in meeting the requirements of security risk monitoring and early warning and information resource sharing and use. On-site real-time safety data has not been effectively collected and applied, and the degree of integration between data and applications is not high.

[0005] (2) Each system has an “island” problem, and the overall data utilization rate is low. For example, the system records the business data of each department of the enterprise in real time. There is a large amount of data in the database and various systems. This data is limited to various modules for the company's future strategic planning, such as personnel management, dangerous operations, hidden danger investigation, training and education, etc. However, the data of a single system (such as a security system) can only express information within a certain range and scenario. It is impossible to combine all the structured and semi-structured data of the enterprise for analysis, compare different dimensions and indicators, and use algorithms to mine the inherent implicit information of the data.

[0006] (3) Risk identification is not timely, tracing is not accurate, and resources are wasted. Some safety information management platforms have not conducted in-depth research and applied safety science principles to build effective and systematic accident prevention and control models. They do not have a deep understanding of the attributes and distribution of risks, resulting in a wide variety of identified risks, consuming a large amount of data and hardware resources, and thus low risk identification efficiency and failure to achieve accurate tracing.

[0007] (4) In addition, the existing risk classification control system cannot visually display the risk situation of each region, and cannot dynamically warn and control major risk points; the system is also unable to display the collected data in an integrated manner, and cannot meet the requirements of unified visualization.

[0008] In view of this, overcoming the defects of the prior art is an urgent problem to be solved in this technical field. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a regional grid-based dynamic risk classification management and control method for metal smelting enterprises.

[0010] The present invention adopts the following technical solutions:

[0011] In a first aspect, the present invention provides a method for regional grid-based dynamic risk classification control in a metal smelting enterprise, which includes setting a central service platform for the enterprise area, dividing the enterprise area into multiple grids, setting an intelligent sensing device at each monitoring location in each grid, and setting a centralized device for one or more grids, wherein the centralized device is used to collect sensor data from the intelligent sensing device and report the collected sensor data to the central service platform; the method includes:

[0012] The central service platform compares the sensor data with the corresponding monitoring threshold to determine whether there is any abnormality in the sensor data;

[0013] First, for each grid, the abnormal sensor data in the corresponding grid is analyzed to obtain the alarm of each grid. Then, combined with the alarm of the upstream grid, the alarm of the downstream grid is filtered out to obtain the final alarm analysis result.

[0014] Preferably, a corresponding grid administrator is assigned to each grid, and the grid administrator is used to perform daily maintenance and alarm processing on the devices in the grid. Grid captains are also designated for multiple grid administrators, and interactive devices are assigned to the grid captains. The interactive devices are used to exchange information with the central service platform. The grid administrators and grid captains are both educated and trained before taking up their posts. The method further includes:

[0015] The central service platform conducts training effectiveness tests on the corresponding grid administrators at corresponding intervals. The training effectiveness tests specifically include:

[0016] The central service platform selects a first alarm item from the preset alarm table items, and reversely generates test data based on the first alarm item; the test data is the sensor data that causes the first alarm item to be abnormal;

[0017] The test data is sent to a centralized device so that the centralized device sends the test data to the corresponding intelligent sensor device, and the intelligent sensor device uses the test data instead of the actually collected sensor data to display on the dial, so that the grid administrator can observe abnormal data through the dial of the intelligent sensor device and report the abnormal data to the grid leader, who then reports the abnormal data to the central service platform through the interactive device;

[0018] The central service platform calculates the matching degree between the abnormal data received from the interactive device and the test data;

[0019] Based on the matching degree, the training score of the grid administrator of the corresponding grid is calculated and displayed on the central service platform, so as to judge whether the grid administrator is suitable for the position or whether further education and training is needed through the training score.

[0020] Preferably, the first alarm item is an alarm item whose alarm processing priority is lower than a preset priority and whose alarm level is higher than a preset level.

[0021] Preferably, the central service platform calculates the matching degree between the abnormal data received from the interactive device and the test data, specifically including:

[0022] The matching degree is calculated using the first formula; the first formula is Among them, I t_n is the matching degree obtained from the nth training effect test of the grid administrator, Num(error_data) is the number of abnormal data received by the central service platform from the interactive device, and Num(test_data) is the number of generated test data.

[0023] Preferably, the calculating of the training score of the grid administrator of the corresponding grid according to the matching degree specifically includes:

[0024] Use the second formula to calculate the test results of the grid administrator's training effectiveness test;

[0025] Use the third formula to calculate the training score of the grid administrator after the training effect test;

[0026] The second formula is I' t_n =k1I t_n +k2I' t_n-1 , the third formula is I g_n =k3I' t_n +k4I r ; k1, k2, k3 and k4 are corresponding weight values, where k1+k2=1, k3+k4=1, I′ t_n is the test result of the nth training effect test, I r is the evaluation score of the grid administrator after training, I g_n is the training score of the grid administrator after the nth training effect test, I t_n is the matching degree obtained from the nth training effect test of the grid administrator, I' t_n-1 This is the test result of the n-1th training effectiveness test for grid administrators.

[0027] Preferably, during the training effect test, the central service platform still receives sensor data, and when it is analyzed that an alarm occurs in the grid where the training effect test is being performed, the training effect test is terminated to display the sensor data collected by the intelligent sensor device on the dial;

[0028] The grid captain is informed of the time of the training effect test through the interactive device so that the grid captain can notify the grid administrator to terminate the training effect test and thus handle the alarm in time.

[0029] Preferably, the central service platform conducts a training effect test on the corresponding grid administrators at corresponding intervals, specifically including:

[0030] At intervals of a first preset period, a random number is generated for the grid. If the generated random number is a prime number, statistics are then generated to determine whether device configuration information of the grid has changed within a second preset period before the current moment, or whether an alarm has been generated by the grid within the second preset period before the current moment;

[0031] If the device configuration information of the grid has not changed within the second preset period before the current moment, and the grid has not generated an alarm, then a training effect test is conducted on the grid administrator of the grid;

[0032] The generating of random numbers specifically includes:

[0033] A seed number is obtained by adding the grid code to the current time, a random intermediate number is generated using the seed number, and a modulo M is obtained using the random intermediate number to obtain the random number;

[0034] in, T0 is a preset value, T is a third preset period, times is the number of alarms in the grid in the third preset period, and when no alarm occurs in the grid in the third preset period before the current moment, M=T0×T.

[0035] Preferably, the method first performs alarm analysis on abnormal sensor data in each grid to obtain alarms for each grid, and then filters out the alarms of the downstream grid in combination with the alarms of the upstream grid to obtain the final alarm analysis results, which specifically include:

[0036] Using all abnormal sensor data, an abnormal sensor code is generated; wherein each bit in the abnormal sensor code corresponds to one sensor data, and when the corresponding sensor data is abnormal, the corresponding bit in the abnormal sensor code is set to 1, otherwise, the corresponding bit is set to 0, thereby forming the abnormal sensor code;

[0037] Performing an AND operation on the abnormality sensing code and each alarm code in the preset alarm table to obtain an operation result. If the operation result is greater than 0, analyzing and obtaining the alarm corresponding to the alarm code;

[0038] Use all alarms to generate abnormal alarm codes, perform AND operations on the abnormal alarm codes and the associated alarm abnormal codes corresponding to each alarm in a preset order, and if the operation result is greater than 0, filter out the alarm corresponding to the associated alarm abnormal code;

[0039] Among them, the preset alarm table items include the monitoring threshold corresponding to each sensor data, the alarm code of each alarm item and the associated alarm exception code of each alarm item; among them, in the alarm code of the corresponding alarm item, the bits corresponding to all the sensor data that cause the alarm item to be abnormal are 1, and the other bits are 0; in the associated alarm exception code of the corresponding alarm item, the bits corresponding to all other alarms that cause the alarm are 1, and the other bits are 0.

[0040] In a second aspect, the present invention further provides a regional grid-based dynamic risk grading control device for a metal smelting enterprise, which is used to implement the regional grid-based dynamic risk grading control method for a metal smelting enterprise described in the first aspect, and the device comprises:

[0041] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the method for regional grid-based dynamic risk grading control of metal smelting enterprises described in the first aspect.

[0042] In a third aspect, the present invention further provides a non-volatile computer storage medium, wherein the computer storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors to complete the method described in the first aspect.

[0043] In a fourth aspect, a chip is provided, comprising: a processor and an interface, for calling and running a computer program stored in a memory from a memory, and executing the method as described in the first aspect.

[0044] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer or a processor, causes the computer or the processor to execute the method as described in the first aspect.

[0045] The present invention divides the grids, first performs alarm analysis on each grid, and then filters the alarms of the downstream grid in combination with the alarms of the upstream grid, thereby effectively eliminating the number of duplicate alarms and determining the grid where the actual alarm is located, thereby assigning responsibility to the specific grid and facilitating alarm processing by grid managers. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0047] Figure 1 This is a schematic diagram of a first method for regional grid-based dynamic risk classification management and control of a metal smelting enterprise provided by an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of a second method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0049] Figure 3 This is a flow chart of a first method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the architecture of intelligent sensing equipment in a regional grid-based dynamic risk classification management and control method for a metal smelting enterprise provided by an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the relationship between various regions in a method for regional grid-based dynamic risk classification management and control of a metal smelting enterprise provided by an embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram of equipment in a blast furnace workshop in a regional grid-based dynamic risk classification management and control method for a metal smelting enterprise provided by an embodiment of the present invention;

[0053] Figure 7 This is a schematic diagram of a first preset alarm table item in a method for regional grid-based dynamic risk classification management and control of a metal smelting enterprise provided by an embodiment of the present invention;

[0054] Figure 8 This is a schematic diagram of a second preset alarm table item in a method for regional grid-based dynamic risk classification management and control of a metal smelting enterprise provided by an embodiment of the present invention;

[0055] Figure 9 This is a flow chart of a second method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0056] Figure 10 This is a flow chart of a third method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0057] Figure 11This is a schematic diagram of test data in a method for regional grid-based dynamic risk classification management and control of a metal smelting enterprise provided by an embodiment of the present invention;

[0058] Figure 12 This is a schematic diagram of the scores corresponding to the education and training levels in a regional grid-based dynamic risk grading management and control method for a metal smelting enterprise provided by an embodiment of the present invention;

[0059] Figure 13 This is a schematic diagram of the scores corresponding to the education and training time ratio in a regional grid-based dynamic risk classification management and control method for a metal smelting enterprise provided by an embodiment of the present invention;

[0060] Figure 14 This is a flow chart of a fourth method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0061] Figure 15 This is a flow chart of a fifth method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0062] Figure 16 Schematic diagram of a third method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0063] Figure 17 This is a flow chart of a sixth method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0064] Figure 18 Schematic diagram of a fourth method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0065] Figure 19 Schematic diagram of a fifth method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0066] Figure 20 This is a flow chart of a seventh method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0067] Figure 21 This is a schematic diagram of a first preset alarm table item in another method for regional grid-based dynamic risk classification management and control of a metal smelting enterprise provided by an embodiment of the present invention;

[0068] Figure 22 2 is a schematic diagram of a second preset alarm table item in another method for regional grid-based dynamic risk classification management and control of a metal smelting enterprise provided by an embodiment of the present invention;

[0069] Figure 23This is a flow chart of an eighth method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0070] Figure 24 This is a flow chart of a ninth method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0071] Figure 25 This is a flow chart of a tenth method for regional grid-based dynamic risk classification management and control of metal smelting enterprises provided by an embodiment of the present invention;

[0072] Figure 26 Schematic diagram of a second preset alarm table item in another method for regional grid-based dynamic risk classification management and control of a metal smelting enterprise provided by an embodiment of the present invention;

[0073] Figure 27 This is a schematic diagram of the architecture of a regional grid-based dynamic risk classification control device for a metal smelting enterprise provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] Unless the context requires otherwise, throughout the specification and claims, the term "including" is to be interpreted as meaning open inclusion, that is, "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "example", "specific example" or "some examples" and the like are intended to indicate that the specific features, structures, materials or characteristics associated with the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representation of the above terms does not necessarily refer to the same embodiment or example. In addition, the specific features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order and position of appearance, it is not limited to that they can be carried in combination by one embodiment or example.

[0076] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, "multiple" means two or more. In addition, for example, the description may also use the method of adding "A" and "B" at the end to describe the same type of nouns as two independent individuals. In this case, the corresponding features defined as "A" and "B" are only used to distinguish the description purposes of the same type of individuals, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated.

[0077] In the description of the present invention, the expression "A and / or B" (where A and B are used to formally represent specific characteristic contents) will be involved, and the corresponding expressions include the following three combinations: only A, only B, and a combination of A and B.

[0078] As used herein, "about," "substantially," or "approximately" includes the stated value and an average value that is within an acceptable range of deviation from the particular value as determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0079] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0080] Embodiment 1:

[0081] Embodiment 1 of the present invention provides a method for regional grid-based dynamic risk classification control of a metal smelting enterprise, which sets a central service platform for the enterprise area, divides the enterprise area into multiple grids, sets intelligent sensor equipment at each monitoring position of each grid, and sets a centralized device for one or more grids, wherein the centralized device is used to collect sensor data of the intelligent sensor equipment and report the collected sensor data to the central service platform; in a specific application scenario, one grid corresponds to one centralized device, such as Figure 1 As shown, in another more specific embodiment, the centralized device may include one or more devices, and the reporting of sensor data is realized through the network connection relationship between the multiple devices, such as Figure 2As shown, the centralized equipment can be one or more of a distributed control system (DCS), a gas detection alarm system (GCS), network equipment such as OPC equipment, an Internet host, a switch, and many other devices. The specific division of the grid is determined by those skilled in the art based on the analysis of the degree of correlation between the various areas in the enterprise area. Figure 3 As shown, the method includes:

[0082] In step 201, the central service platform compares the sensor data with the corresponding monitoring threshold to determine whether there is an anomaly in the sensor data; the monitoring threshold of each sensor data is obtained by a person skilled in the art based on empirical analysis. The intelligent sensor device includes a sensor, a control mainboard, and a display dial. The input end of the control mainboard is connected to the output end of the sensor. The control mainboard is also connected to the corresponding centralized device through a network function. The sensor is used to collect sensor data of the monitoring location and send the sensor data to the control mainboard. The control mainboard sends the sensor data to the centralized device, and the centralized device then sends the sensor data to the central service platform. The centralized device and each intelligent sensor device can be connected by wired means or local networking, such as using Zigbee networking. The centralized device and the central service platform are connected through TCP communication.

[0083] One control terminal of the control mainboard is also connected to the display panel to display the sensor data on the display panel. The control mainboard can also receive test data from the centralized equipment and display the test data on the display panel. Figure 4 shown.

[0084] In step 202, first, for each grid, an alarm analysis is performed on abnormal sensor data in the corresponding grid to obtain an alarm for each grid.

[0085] In step 203, the alarms of the downstream grid are filtered out in combination with the alarms of the upstream grid to obtain the final alarm analysis result.

[0086] Among them, the steps 202-203 can be understood as first analyzing each grid to determine the possible alarms in the grid (i.e., the reasons within the grid that cause the abnormality of each sensor data). However, in actual use, due to the mutual correlation between grids, the alarm of the downstream grid may also be caused by the alarm in the upstream grid. For example, when the output material of the upstream grid enters the downstream grid for processing, if the output material of the upstream is abnormal due to the corresponding alarm, it will cause an alarm in the downstream grid. At this time, the source of the alarm of the downstream grid is the alarm of the upstream grid. Therefore, the above step 203 is used to filter out such alarms of the downstream grid, thereby reducing the number of duplicate alarms and presenting clear alarm analysis results to the user. Taking some areas of steel smelting in a smelting enterprise as an example, Figure 5 As shown in the figure, it includes multiple areas such as sintering area, pelletizing area, ironmaking area, steelmaking area, rolling area and coking area. There are upstream and downstream relationships between these multiple areas. For example, the molten iron produced in the ironmaking area is used as the input of the steelmaking area for steelmaking operations. For example, within the area, each workshop also has an upstream and downstream relationship. For example, the upstream workshops of the blast furnace workshop include the blast equipment workshop and the hot blast furnace workshop, and the output of these upstream workshops will affect the downstream workshops. For example, when the blast equipment workshop generates an alarm, resulting in a decrease in the cold air output flow, it will cause the cold air inlet of the blast furnace workshop (such as Figure 6 Insufficient cold air flow may also cause the hot air outlet temperature to be too high.

[0087] This embodiment divides the grids, first performs alarm analysis on each grid, and then filters the alarms of the downstream grids in combination with the alarms of the upstream grids, thereby effectively eliminating the number of duplicate alarms and determining the grid where the actual alarm is located, thereby assigning responsibility to the specific grid and facilitating alarm processing by grid managers.

[0088] In an optional application scenario, each sensor data and each alarm are managed using corresponding entries, such as using a first preset alarm entry to manage each sensor data, and using a second preset alarm entry to manage each alarm item. The first preset alarm entry and the second preset alarm entry are collectively referred to as preset alarm entries. The first preset alarm entry is as follows: Figure 7As shown, it includes a sensor sequence number, sensor data (also called sensor data name), monitoring indicators and monitoring thresholds (including a monitoring threshold lower limit and a monitoring threshold upper limit). Among them, one sensor data can correspond to one or more monitoring indicators, and each monitoring indicator corresponds to a unique sensor sequence number. When the corresponding monitoring indicator only monitors the upper threshold limit or only monitors the lower threshold limit, the side that does not need to be monitored is set to the default value (such as -1). For example, for CO concentration, if only the upper threshold limit is monitored, the lower threshold limit is set to -1. When the sensor data is subsequently compared with the corresponding monitoring threshold, there is no need to compare the upper threshold limit and the lower threshold limit with the default value in the first preset alarm table item.

[0089] The second preset alarm table entry is as follows Figure 8 As shown, it includes a grid number, an alarm number, an alarm item, an alarm code and an associated alarm exception code; a grid number represents a grid, which contains multiple alarm items, each alarm item corresponds to a unique alarm number, and each alarm item contains an alarm code, which is obtained by a technician in this field based on experience analysis. Specifically: find multiple sensor data that trigger the corresponding alarm item, and set the bits corresponding to the positions of the multiple sensor data in the alarm code of the alarm item to 1, and set the other bits to 0. For example, assuming that there are M monitoring indicators in the first preset alarm table item, the length of each alarm code is M bits. When the sensor numbers of the multiple sensor data that trigger the corresponding alarm item are m1, m2, ..., m i When the alarm code of the alarm item is m1+1, m2+1, ..., m i +1 bit is set to 1, and the other bits are set to 0.

[0090] Similarly, the associated alarm exception code is also obtained by those skilled in the art based on empirical analysis. Specifically, find the alarm item that triggers the corresponding alarm (usually the alarm item of the upstream grid), set the bit corresponding to the alarm item to 1, and set the other bits to 0. For example, if there are N alarm items in the second preset alarm table item, the length of each associated alarm exception code is N bits. When the alarm item (for distinction, hereinafter referred to as the second alarm item) that triggers the alarm of the corresponding alarm item (for distinction, hereinafter referred to as the first alarm item) has an alarm sequence number of n1, n2, ..., n respectively. j , then the n1+1, n2+1, ..., nth j +1 bit is set to 1, and the other bits are set to 0.

[0091] Sensor data can be understood as the surface manifestations directly brought about by sensor data, and alarm items can be understood as the deep-seated reasons that lead to abnormalities in the corresponding sensor data.

[0092] The method first analyzes the abnormal sensor data in each grid to obtain the alarm of each grid, and then filters the alarm of the downstream grid in combination with the alarm of the upstream grid to obtain the final alarm analysis result, such as Figure 9 As shown, specifically including:

[0093] In step 301, all abnormal sensor data are used to generate an abnormal sensor code; wherein each bit in the abnormal sensor code corresponds to one sensor data, and when the corresponding sensor data is abnormal, the corresponding bit in the abnormal sensor code is set to 1, otherwise, the corresponding bit is set to 0 to form the abnormal sensor code; the bit corresponding to each sensor data is determined by the sensor sequence number.

[0094] In step 302, the abnormal sensor code is ANDed with each alarm code in the preset alarm table item to obtain an operation result. If the operation result is greater than 0, the alarm corresponding to the alarm code is analyzed and obtained; when the operation result is greater than 0, it means that all sensor data corresponding to the alarm code are abnormal, and it is considered that these abnormalities are caused by the alarm item corresponding to the alarm code, triggering the alarm of the alarm item.

[0095] In step 303, all alarms are used to generate an abnormal alarm code. This abnormal alarm code is then ANDed with the associated alarm abnormality code corresponding to each alarm in a predetermined order. If the result of this ANDing operation is greater than 0, the alarm corresponding to the associated alarm abnormality code is filtered out. The predetermined order can be represented by the alarm sequence number. If the result of this ANDing operation is greater than 0, it is assumed that the alarm that triggered the alarm exists in the upstream grid. The alarm in the upstream grid is then identified as the true cause, and the alarm in the downstream grid is merely a knock-on effect of the upstream grid's alarm. Therefore, the downstream grid's alarm is filtered out.

[0096] Among them, the preset alarm table items include the monitoring threshold corresponding to each sensor data, the alarm code of each alarm item and the associated alarm exception code of each alarm item; among them, in the alarm code of the corresponding alarm item, the bits corresponding to all the sensor data that cause the alarm item to be abnormal are 1, and the other bits are 0; in the associated alarm exception code of the corresponding alarm item, the bits corresponding to all other alarms that cause the alarm are 1, and the other bits are 0.

[0097] In actual use, the purpose of dividing the grids is to better manage them. That is, a corresponding grid administrator is assigned to each grid. The grid administrator is responsible for performing daily maintenance and alarm processing on the devices in the grid. Grid captains are also designated for multiple grid administrators. Interactive devices are assigned to the grid captains. The interactive devices are used to exchange information with the central service platform. The grid administrators and grid captains are trained before taking up their posts. The method also includes:

[0098] The central service platform conducts training effect tests on the corresponding grid administrators at corresponding intervals, such as Figure 10 As shown, the training effect test specifically includes:

[0099] In step 401, the central service platform selects the first alarm item from the preset alarm table items, and reversely generates test data based on the first alarm item; the test data is the sensor data that causes the first alarm item to be abnormal; the central service platform selects the first alarm item from the preset alarm table items, and reversely generates test data based on the first alarm item, specifically including: selecting the first alarm item from the second preset alarm table item, and finding the corresponding sensor data (that is, the sensor data corresponding to the bit with a value of 1 in the alarm code) according to the alarm code of the first alarm item, and then finding the monitoring threshold corresponding to these sensor data from the first preset alarm table item, and generating a value outside the monitoring threshold range as test data.

[0100] In step 402, the test data is sent to the centralized device so that the centralized device sends the test data to the corresponding intelligent sensor device, and the intelligent sensor device uses the test data to replace the actually collected sensor data and displays it on the dial, so that the grid administrator can observe abnormal data through the dial of the intelligent sensor device and report the abnormal data to the grid leader, who reports the abnormal data to the central service platform through the interactive device; wherein, while the intelligent sensor device displays the test data on the dial, the intelligent sensor device still collects sensor data and transmits the sensor data to the central service platform through the centralized device.

[0101] When sending the test data to the centralized device, the data ID of the test data (i.e., the data ID of the sensor data) is also sent. The sensor serial number can be used directly as the data ID, or a sensor ID can be additionally determined for each sensor data. For example, the UUID of the corresponding smart sensor device is used as the sensor ID. The sensor ID and the test data value are sent to the centralized device together. In actual use, the data format sent to the centralized device is as follows: Figure 11 As shown, it includes dataId (i.e. sensor ID), value (i.e. test data value) and other data (such as enterprise code, timestamp, etc.). For example, the Data field in a message sent from the central service platform to the centralized device is as follows:

[0102] {"dataId":"90134c28c31b49ea85e17bb90ff32eef","enterpriseId":"123456789","gat ewayId":"123456789","collectTime":"20180615123456","isConnectDataSource":true,"reportType":"report","datas":[{"quotaId":"123","value":123.0},{"quotaId":"321","value":123.0}]}

[0103] In actual use, this field is also encrypted using ASE, resulting in the following content:

[0104] {"appId":"xxxxxxxxx","serviceId":"xxxxxxxxxx","dataId":"1563259577529","data":"BLnaeC X2a0t

[0105] This message represents the transmission of test data for a sensor with the ID 90134c28c31b49ea85e17bb90ff32eef and a value of 123.0. The centralized device finds the corresponding smart sensor device based on the sensor ID and sends the test data to the smart sensor device, which then displays it on the dial.

[0106] In actual use, the reporting of sensor data also uses Figure 11 The data format shown is implemented as follows: the sensor data reported by the smart sensor device to the central service platform via the centralized device is sensor data, and the test data sent from the central service platform to the smart sensor device via the centralized device is test data. Furthermore, the test data is sent continuously: the smart sensor device displays each test data on the dial after receiving it. If it detects that no new test data has been received within a certain period of time since the last test data was received, it switches to displaying the sensor data on the dial. This allows the smart sensor device to manage the switching of display modes. This avoids the issue of being unable to switch back to the sensor data display mode due to network interruption when the upstream is in the test data display mode and managing the display mode switch via commands.

[0107] In actual use, since the grid captain also needs to perform routine maintenance on the equipment in their own grid, to facilitate their mobility, the interactive device is a portable device, such as a mobile terminal. In actual use, the interactive device can be installed on the grid captain's mobile phone as an application. The interactive device is used to receive pending tasks from the central service platform, namely, pending alarms for the grids under the grid captain's jurisdiction, pending alarms for grids under the grid captain's jurisdiction, and daily maintenance tasks. The abnormal data can be understood as the data on the dial observed by the grid administrator.

[0108] In step 403, the central service platform calculates the matching degree between the abnormal data received from the interactive device and the test data; wherein the central service platform calculates the matching degree between the abnormal data received from the interactive device and the test data, specifically including:

[0109] The matching degree is calculated using the first formula; the first formula is Among them, I t_n is the matching degree obtained from the nth training effect test of the grid administrator, Num(error_data) is the number of abnormal data received by the central service platform from the interactive device, and Num(test_data) is the number of test data generated. In actual use, there is also an optional implementation method: combining the difference between the value of the abnormal data and the value of the test data, and making this difference participate in the calculation of the matching degree. In this case, the first formula is Among them, k'1 and k'2 are weights obtained by those skilled in the art based on empirical analysis, error_data i is the i-th abnormal data, test_data i is the i-th test data. When there is no i-th abnormal data, let The value of is 1.

[0110] In actual use, the grid administrator also analyzes the causes of the abnormal data based on training and learning, and reports the analyzed abnormal causes to the grid captain. The grid captain reports the abnormal causes to the central service platform through the interactive device. The central service platform also uses the pre-trained semantic analysis model to analyze the abnormal causes, obtains the corresponding semantic vector, and calculates the vector distance D between the semantic vector and the first alarm item. The first formula is Here, k'3 is a weight obtained by those skilled in the art based on empirical analysis.

[0111] In step 404, the training score of the grid administrator of the corresponding grid is calculated based on the matching degree, and the training score is displayed on the central service platform so that the grid administrator can be judged whether he is suitable for the position or whether he needs further education and training based on the training score.

[0112] This embodiment tests the effectiveness of training grid administrators, thereby determining whether the grid administrators can effectively manage their grids, thereby effectively managing personnel and improving the security of grid area management.

[0113] In an optional implementation, the matching degree can be directly used as the training score of the corresponding grid administrator. The higher the matching degree, the better the training effect of the grid administrator and the more adaptable the grid administrator is to the position. There is also a preferred implementation as follows:

[0114] Calculating the training score of the grid administrator of the corresponding grid according to the matching degree specifically includes:

[0115] The second formula is used to calculate the test results of the grid administrator's training effectiveness test; the third formula is used to calculate the training score of the grid administrator after the training effectiveness test.

[0116] The second formula is I' t_n =k1I t_n +k2I' t_n-1 , the third formula is I g_n =k3I' t_n +k4I r ; k1, k2, k3 and k4 are corresponding weight values, where k1+k2=1, k3+k4=1, I′ t_n is the test result of the nth training effect test, I r The evaluation score of the grid administrator after training is usually obtained by the trainer after the training. The score is directly entered into the central service platform. g_n is the training score of the grid administrator after the nth training effect test, I t_n is the matching degree obtained from the nth training effect test of the grid administrator, I' t_n-1 This is the test result of the n-1th training effectiveness test for grid administrators.

[0117] In actual use, the evaluation score I r It is also combined with the level of education and training and the time ratio of education and training to obtain the specific results: r=k5I4+k6I5, where k5 and k6 are corresponding weight values, which are obtained by those skilled in the art based on empirical analysis. I4=D1d1+D2d2+D3d3, where I4 is the result of the education and training level calculation, D n d is the number of times of education and training at the company level, workshop (department, branch) level, and team level, respectively. n The scores for company-level, workshop (department, branch)-level, and team-level education and training are n=1, 2, 3, and each D n Corresponding d n The value of Figure 12 shown.

[0118] I5 is the calculation result of the education and training time ratio, is the score corresponding to the company-level education and training time ratio, n1=1, 2, 3, 4, 5, is the score corresponding to the workshop (department, branch) level education and training time ratio, n2=1, 2, 3, 4, 5, is the score corresponding to the team-level education and training time ratio, n3=1, 2, 3, 4, 5, and The corresponding scores are Figure 13 shown.

[0119] In some application scenarios, when an alarm occurs, for some locations that cannot be intelligently monitored, grid administrators are required to use corresponding monitoring tools to reach the monitoring location to conduct monitoring and record. In order to further quantify the adaptability of grid administrators to their positions, this embodiment also provides a preferred implementation method. In this preferred implementation method, the processing actions of grid administrators after detecting abnormal data are also included in the evaluation. The preferred implementation method is as follows:

[0120] The matching degree between the abnormal data received from the interactive device and the test data is calculated as a first matching degree.

[0121] A UWB tag is pre-assigned to each grid administrator; the central service platform also determines the movement trajectory of each grid administrator after arriving at the target intelligent sensing device based on the UWB tag; the movement trajectory is matched with a preset map to determine the monitoring locations passed by the movement trajectory; and a second matching degree between each monitoring location and the preset processing behavior of the first alarm item is calculated; wherein the target intelligent sensing device is an intelligent sensing device that displays test data.

[0122] A training score of a grid administrator of the corresponding grid is calculated based on the first matching degree and the second matching degree.

[0123] The calculating of the second matching degree between each monitoring location and the preset processing behavior of the first alarm item specifically includes:

[0124] The second matching degree is calculated using the second formula; the second formula is Among them, I p_n is the second matching degree obtained from the nth training effect test of the grid administrator, Num(test_pos) is the number of first monitoring positions that the grid administrator needs to manually monitor as specified in the preset processing behavior, and Num(passed_test_pos) is the number of first monitoring positions passed by the grid administrator's movement trajectory.

[0125] The preset map is formed by technicians in this field by calibrating each monitoring location on a grid map, and the preset processing behavior is obtained by technicians in this field based on empirical analysis, and is specifically manifested as a collection of one or more monitoring locations on the preset map.

[0126] Calculating the training score of the grid administrator of the corresponding grid according to the first matching degree and the second matching degree specifically includes:

[0127] The third formula is used to calculate the test results of the grid administrator's training effectiveness test.

[0128] Use the fourth formula to calculate the training score of the grid administrator after this training effect test.

[0129] The third formula is I' t_n =k1I t_n +k2I p_n +k3I' t_n-1 , the fourth formula is I g_n =k4I' t_n +k5I r ; k1, k2, k3, k4 and k5 are corresponding weight values, where k1+k2+k3=1, k4+k5=1, I′ t_n is the test result of the nth training effect test, I r is the evaluation score of the grid administrator after training, I g_n is the training score of the grid administrator after the nth training effect test, I t_n is the first matching degree obtained from the nth training effect test of the grid administrator, I p_n is the second matching degree obtained from the nth training effect test of the grid administrator, I′ t_n-1 This is the test result of the n-1th training effectiveness test for grid administrators.

[0130] Considering that in actual use, different alarms are handled differently, for alarms with lower alarm levels, grid administrators often only need to handle them by themselves, without the need to report and analyze. Similarly, there are some alarms that are very likely to cause security incidents if they are not handled in a timely manner. Therefore, once data anomalies are detected, they need to be handled immediately and reported after the processing is completed. Once these alarms appear, they will directly affect the working conditions of the equipment. In order to avoid the impact of training effect testing on the working conditions of the equipment, the first alarm item is an alarm item with an alarm processing priority lower than the preset priority and an alarm level higher than the preset level. The preset priority and preset level are obtained by technical personnel in this field based on empirical analysis. It can be considered that: when the alarm processing priority is higher than or equal to the preset priority, the grid administrator needs to handle it immediately. When the alarm level is lower than or equal to the preset level, the grid administrator does not need to report.

[0131] In some application scenarios, during the training effect test, the central service platform still receives sensor data. When the analysis shows that an alarm occurs in the grid where the training effect test is being conducted, the training effect test is terminated to display the sensor data collected by the intelligent sensor equipment on the dial.

[0132] The grid captain is informed of the time of the training effect test through the interactive device so that the grid captain can notify the grid administrator to terminate the training effect test and thus handle the alarm in time.

[0133] In order to ensure that the training effect test does not affect the conventional data monitoring, this embodiment also provides the following preferred implementation method, that is, the central service platform performs a training effect test on the corresponding grid administrator at corresponding intervals, such as Figure 14 As shown, specifically including:

[0134] In step 501, a random number is generated for each grid at intervals of a first preset period. If the generated random number is a prime number, statistics are then collected to determine whether the grid's device configuration information has changed or whether the grid has generated an alarm within a second preset period prior to the current moment. The first and second preset periods are determined empirically by those skilled in the art. The central service platform assigns a timer to each grid and sequentially activates the timer to time the first preset period, staggering the training effectiveness testing times for each grid.

[0135] In step 502, if the device configuration information of the grid has not changed within the second preset period before the current moment and the grid has not generated an alarm, a training effect test is performed on the grid administrator of the grid.

[0136] When the generated random number is a prime number, it is considered that the time has come to conduct a random training effect test, and further judgment is made as to whether the conditions for the training effect test are met at present, that is, whether the devices in the grid are running stably. The judgment standard for stable operation is: if the device runs for the second preset period under a set of configuration information conditions without generating an alarm, then it is considered that the devices in the grid are running stably.

[0137] Wherein, the random number is generated, such as Figure 15 As shown, specifically including:

[0138] In step 601, a seed number is obtained by adding the grid code to the current time, and a random intermediate number is generated using the seed number.

[0139] In step 602, the random intermediate number is used to modulo M to obtain the random number.

[0140] in, T0 is a preset value, T is a third preset period, times is the number of alarms in the grid in the third preset period, and when no alarm occurs in the grid in the third preset period before the current moment, M=T0×T.

[0141] The third preset period is determined by those skilled in the art based on empirical analysis. The third preset period is much longer than the second preset period and the first preset period. In actual use, the third preset period is typically one week, one month, or multiple months. The preset value is determined by those skilled in the art based on demand analysis.

[0142] Among them, when the number of alarms in the third preset period is greater, the M value is smaller, and the corresponding probability that the generated random number is a prime number is greater. In this way, the number of times the grid administrators in the grid with a large number of alarms conduct training effect tests is increased.

[0143] Example 2:

[0144] On the basis of Example 1, this embodiment also provides another regional grid-based dynamic risk classification control method for metal smelting enterprises, which sets up a central service platform for the entire blast furnace area, divides the blast furnace workshop into multiple grids, and sets up intelligent sensing equipment at each monitoring position of each grid, such as Figure 16 As shown, the intelligent sensor device is used to collect sensor data of the monitoring location and report the sensor data to the central service platform; the method described in this embodiment is suitable for scenarios where the enterprise is small in scale or the distance between the intelligent sensor device and the central service platform is relatively close, such as Figure 17 As shown, the method includes:

[0145] In step 701, the central service platform receives the sensor data of each intelligent sensor device, and establishes a digital twin model of each grid based on the sensor data of each grid; wherein, the digital twin model is presented to the user in the form of image modeling combined with data presentation.

[0146] In step 702, the central service platform also compares the sensor data with the corresponding monitoring threshold to determine whether there is any abnormality in the sensor data; performs alarm analysis on the abnormal sensor data in the corresponding grid, and obtains the alarm of each grid and the corresponding alarm risk level.

[0147] In step 703, the corresponding areas of the grid in the digital twin model are color-coded according to the alarm risk level of each alarm in the grid, so that the user can intuitively confirm the risk status of the grid through the digital twin model; among them, the alarm risk level includes four levels: no risk, low level, medium level and high level.

[0148] In step 704, when the highest alarm risk level in the corresponding grid is high, the corresponding area of ​​the grid in the digital twin model is marked as red; when the highest alarm risk level in the corresponding grid is medium, the corresponding area of ​​the grid in the digital twin model is marked as orange; when the highest alarm risk level in the corresponding grid is low, the corresponding area of ​​the grid in the digital twin model is marked as yellow; when the highest alarm risk level in the corresponding grid is no risk, the corresponding area of ​​the grid in the digital twin model is marked as blue, as shown in FIG. Figure 18 As shown in the figure, an image model is established according to the distribution of each device and plant in the blast furnace workshop. The corresponding grid is marked with a corresponding color in the image model, and when the user clicks on a grid, the sensor data of each device in the grid is displayed to the user. In actual use, the digital twin model also presents the interior of each grid, such as Figure 19 As shown, the image model and the corresponding sensor data are presented.

[0149] This embodiment sets multiple alarm risk levels and establishes a digital twin model. The alarm risk level of each grid is identified by color in the digital twin model, so that users can intuitively observe the risk status of each grid.

[0150] In an optional embodiment, the central service platform also compares the sensor data with the corresponding monitoring threshold to determine whether the sensor data is abnormal; performs alarm analysis on the abnormal sensor data in the corresponding grid, and obtains the alarm of each grid and the corresponding alarm risk level, such as Figure 20 As shown, specifically including:

[0151] In step 801, the monitoring threshold includes a high-level monitoring threshold, an intermediate monitoring threshold and a low-level monitoring threshold. The sensor data is compared with the high-level monitoring threshold, the intermediate monitoring threshold and the low-level monitoring threshold in turn. When the comparison shows that the sensor data exceeds the range of the corresponding monitoring threshold, it is judged that the sensor data is abnormal, and the risk level of the exceeded monitoring threshold is used as the abnormality level of the sensor data; when the comparison shows that the sensor data does not exceed the range of each monitoring threshold, it is judged that the sensor data is not abnormal.

[0152] In step 802, all abnormal sensor data are used to generate an abnormal sensor array; wherein, each value in the abnormal sensor array corresponds to a sensor data, and when there is no abnormality in the sensor data, the corresponding value in the abnormal sensor array is set to 0; when the abnormality level of the sensor data is low, the corresponding value in the abnormal sensor array is set to 1; when the abnormality level of the sensor data is medium, the corresponding value in the abnormal sensor array is set to 2; when the abnormality level of the sensor data is high, the corresponding value in the abnormal sensor array is set to 3.

[0153] In step 803, the abnormal sensor array is used to subtract each alarm code array in the preset alarm table item to obtain a result array. If all arrays in the result array are greater than or equal to 0, the analysis shows that the alarm corresponding to the alarm code array has occurred, and the alarm risk level of the alarm is the alarm risk level corresponding to the alarm code array; wherein, the abnormal sensor array minus the alarm code array is specifically: each value in the abnormal sensor array is subtracted from the corresponding value in the alarm code array, that is, the nth value in the abnormal sensor array is subtracted from the nth value in the alarm code array to obtain the nth value in the result array.

[0154] The preset alarm table entry includes a first preset alarm table entry and a second preset alarm table entry. The first preset alarm table entry is as follows: Figure 21 As shown, including sensor sequence number, sensor data, monitoring indicators, low-level monitoring threshold (including low-level monitoring threshold lower limit and low-level monitoring threshold upper limit), intermediate monitoring threshold (including intermediate monitoring threshold lower limit and intermediate monitoring threshold upper limit) and advanced monitoring threshold (including advanced monitoring threshold lower limit and advanced monitoring threshold upper limit). When the corresponding monitoring indicator only monitors the upper threshold limit or the upper threshold limit, the party that does not need to be monitored is set to the default value (such as -1).

[0155] The second preset alarm table entry is as follows Figure 22As shown, it includes grid serial number, alarm serial number, alarm risk level and alarm code array, wherein each alarm item corresponds to 3 alarm risk levels and 3 alarm code arrays, and each alarm code array is obtained in advance by technical personnel in this field based on empirical analysis. Specifically, according to the order of sensor serial number, the numerical value corresponding to the abnormal level (i.e., 0, 1, 2 and 3) of the sensor data that triggers the corresponding alarm is used as the corresponding numerical value in the alarm code array.

[0156] In combination with the method described in Example 1, this embodiment further provides a preferred implementation method, namely, assigning a corresponding grid administrator to each grid, the grid administrator being responsible for performing routine maintenance and alarm processing on the devices in the grid, designating grid captains for multiple grid administrators, and assigning interactive devices to the grid captains, the assigned interactive devices being used for information exchange with the central service platform; the grid administrators and grid captains are both educated and trained before taking up their posts; the method further includes:

[0157] The central service platform conducts training effect tests on the corresponding grid administrators at corresponding intervals, such as Figure 23 As shown, the training effect test specifically includes:

[0158] In step 901, the central service platform selects a first alarm item from a preset alarm table, and reversely generates test data based on the first alarm item; the test data is the sensor data that causes the first alarm item to be abnormal.

[0159] In step 902, the test data is sent to the intelligent sensor device, and the intelligent sensor device uses the test data to replace the actually collected sensor data and displays it on the dial, so that the grid administrator can observe abnormal data through the dial of the intelligent sensor device and report the abnormal data to the grid team leader, who then reports the abnormal data to the central service platform through the interactive device.

[0160] In step 903, the central service platform calculates the matching degree between the abnormal data received from the interactive device and the test data.

[0161] In step 904, based on the matching degree, the training score of the grid administrator of the corresponding grid is calculated, and the training score is displayed in the digital twin model, so as to judge whether the grid administrator is suitable for the position or whether further education and training is needed through the training score.

[0162] The calculation methods of the matching degree and the training score are implemented based on the same concept as in Example 1. The methods described in Example 1 are applicable to this embodiment and will not be described in detail here.

[0163] Example 3:

[0164] On the basis of Example 1, this embodiment also provides a third method for regional grid-based dynamic risk classification control of metal smelting enterprises, which sets a central service platform for the enterprise area, divides the enterprise area into multiple grids, sets intelligent sensing equipment at each monitoring location of each grid, and sets centralized equipment for one or more grids, and the centralized equipment is used to collect sensor data of the intelligent sensing equipment and report the collected sensor data to the central service platform, such as Figure 1 As shown; cameras are set up in the material handling area of ​​each grid to collect real-time images of the material handling area, such as Figure 24 As shown, the method includes:

[0165] In step 1001, the central service platform establishes a digital twin model of each grid based on the sensor data of each grid; wherein the digital twin model is presented to the user in the form of image modeling combined with data presentation; the material handling area refers to the area in the grid where material handling vehicles may enter and exit.

[0166] In step 1002, the central service platform also compares the sensor data with the corresponding monitoring threshold to determine whether there is any abnormality in the sensor data; the sensor data with abnormalities is input into a pre-trained alarm analysis model to perform alarm analysis on each grid to obtain the alarm of each grid and the corresponding alarm risk level.

[0167] In step 1003, the corresponding area of ​​the grid in the digital twin model is color-coded according to the alarm risk level of each alarm in the grid, so that the user can intuitively confirm the risk status of the grid through the digital twin model.

[0168] In step 1004, the central service platform also receives real-time images of the material handling areas of each grid, and uses a pre-trained image recognition model to perform image recognition on the real-time images to detect whether there are material handling vehicles entering the material handling areas of each grid.

[0169] In step 1005, when a material handling vehicle enters, the alarm risk level of each alarm in the grid is increased by one level, and the increased alarm risk level is displayed on the digital twin model.

[0170] Since material handling vehicles in smelting enterprises often carry flammable or explosive materials, such as coking oil, when a security incident occurs in a corresponding area, if material handling vehicles are also present in the area, there is a high probability that the incident will escalate. Therefore, when a material handling vehicle enters, the alarm risk level of each alarm in the grid is increased to increase the grid administrator's attention to the grid's operation. The image recognition model can be a deep learning model, such as a convolutional neural network (CNN).

[0171] For some smelting enterprises that involve a wide range of fields and have relatively more correlations between equipment, the number of alarm items is usually large, and the relationship between each alarm item may be relatively more complex. The alarm analysis method in Example 1 and Example 2 may be difficult to cope with the analysis and processing of large-scale and highly complex alarm items. Therefore, this embodiment uses an alarm analysis model to perform alarm analysis, so that historical alarm data can be effectively used to analyze and process large-scale and highly complex alarm items, and a digital twin model is established. The digital twin model color-codes the grid according to the alarm, so that users can intuitively confirm the risk situation of each area, and take the dynamic link of material handling into consideration. When material handling occurs, the alarm risk level of the grid is increased, so that users can pay attention to areas with operational hazards in a timely manner.

[0172] The central service platform also compares the sensor data with the corresponding monitoring threshold to determine whether there are any anomalies in the sensor data. The abnormal sensor data is input into a pre-trained alarm analysis model to perform alarm analysis on each grid, and obtain the alarm of each grid and the corresponding alarm risk level. Specifically, the following steps are involved:

[0173] The monitoring thresholds include a high-level monitoring threshold, an intermediate monitoring threshold and a low-level monitoring threshold. The sensor data is compared with the high-level monitoring threshold, the intermediate monitoring threshold and the low-level monitoring threshold in turn until the comparison shows that the sensor data exceeds the range of the corresponding monitoring threshold. In this case, it is judged that the sensor data is abnormal, and the risk level of the exceeded monitoring threshold is used as the abnormality level of the sensor data.

[0174] All abnormal sensor data are used to generate an abnormal sensor vector; wherein, each value in the abnormal sensor vector corresponds to a sensor data, when there is no abnormality in the sensor data, the corresponding value in the abnormal sensor vector is set to 0, when the abnormality level of the sensor data is low, the corresponding value in the abnormal sensor vector is set to 1, when the abnormality level of the sensor data is medium, the corresponding value in the abnormal sensor vector is set to 2, and when the abnormality level of the sensor data is high, the corresponding value in the abnormal sensor vector is set to 3.

[0175] The abnormal sensor vector is input into a pre-trained alarm analysis model to generate an alarm vector. The alarm vector includes the triggering probabilities of alarms at each alarm risk level, with alarms with a triggering probability higher than a preset probability serving as the alarm for each grid. For example, if the alarm item "Blast furnace fuel flow is low" has three alarm risk levels, the alarm vector will contain three corresponding values: the first value is the probability of triggering the "Blast furnace fuel flow is low" alarm with a high alarm risk level, the second value is the probability of triggering the "Blast furnace fuel flow is low" alarm with a medium alarm risk level, and the third value is the probability of triggering the "Blast furnace fuel flow is low" alarm with a low alarm risk level. If the triggering probabilities of multiple alarm risk levels for the same alarm item are all higher than the preset probability, the alarm item with the highest alarm risk level will be used as the analyzed alarm.

[0176] The alarm analysis model may be a deep neural network model. Experts generate a training dataset based on historical sensor data and alarms, and input the training dataset into the deep neural network (DNN) model for training to obtain a pre-trained alarm analysis model. The preset probability is determined by those skilled in the art based on empirical analysis.

[0177] In combination with the method described in Example 1, this embodiment further provides a preferred implementation method, namely, assigning a corresponding grid administrator to each grid, the grid administrator being responsible for performing routine maintenance and alarm processing on the devices in the grid, designating grid captains for multiple grid administrators, and assigning interactive devices to the grid captains, the assigned interactive devices being used for information exchange with the central service platform; the grid administrators and grid captains are both educated and trained before taking up their posts; the method further includes:

[0178] The central service platform conducts training effectiveness tests on the corresponding grid administrators at corresponding intervals. The training effectiveness tests specifically include:

[0179] The central service platform selects a first alarm item from the preset alarm table items, and reversely generates test data based on the first alarm item; the test data is the abnormal sensor data that triggers the first alarm item.

[0180] The test data is sent to a centralized device so that the centralized device sends the test data to the corresponding intelligent sensor device. The intelligent sensor device uses the test data to replace the actually collected sensor data and displays it on the dial, so that the grid administrator can observe abnormal data through the dial of the intelligent sensor device and report the abnormal data to the grid team leader, who then reports the abnormal data to the central service platform through the interactive device.

[0181] The central service platform calculates the matching degree between the abnormal data received from the interactive device and the test data.

[0182] Based on the matching degree, the training score of the grid administrator of the corresponding grid is calculated, and the training score is displayed in the digital twin model, so as to judge whether the grid administrator is suitable for the position or whether further education and training is needed through the training score.

[0183] The calculation methods of the matching degree and the training score are implemented based on the same concept as in Example 1. The methods described in Example 1 are applicable to this embodiment and will not be described in detail here.

[0184] Embodiment 4:

[0185] On the basis of Example 1, this embodiment further provides a fourth method for regional grid-based dynamic risk classification control of metal smelting enterprises, which sets a central service platform for the enterprise area, divides the enterprise area into multiple grids, sets intelligent sensing equipment at each monitoring position of each grid, and sets centralized equipment for one or more grids, wherein the centralized equipment is used to collect sensor data of the intelligent sensing equipment and report the collected sensor data to the central service platform; sets a camera in the material handling area of ​​each grid to collect real-time images of the material handling area; Figure 25 As shown, the method includes:

[0186] In step 1101, the central service platform receives the sensor data of each intelligent sensor device, and establishes a digital twin model of each grid based on the sensor data of each grid; wherein, the digital twin model is presented to the user in the form of image modeling combined with data presentation.

[0187] In step 1102, the central service platform also compares the sensor data with the corresponding monitoring threshold to determine whether there is any abnormality in the sensor data; and first performs alarm analysis on the abnormal sensor data in the corresponding grid for each grid, obtains the alarm of each grid and the corresponding alarm risk level, and then combines the alarm of the upstream grid to filter out the alarm of the downstream grid to obtain the final alarm analysis result.

[0188] In step 1103, the corresponding area of ​​the grid in the digital twin model is color-coded according to the alarm risk level of each alarm in the grid, so that the user can intuitively confirm the risk status of the grid through the digital twin model.

[0189] In step 1104, the central service platform also receives real-time images of the material handling area of ​​each grid, and uses a pre-trained image recognition model to perform image recognition on the real-time images to detect whether there are material handling vehicles entering the material handling area of ​​each grid.

[0190] In step 1105, when a material handling vehicle enters, the alarm risk level of each alarm in the grid is increased by one level; among them, the alarm risk level includes four levels: no risk, low level, medium level and high level; when the highest alarm risk level in the corresponding grid is high level, the corresponding area of ​​the grid in the digital twin model is marked as red, when the highest alarm risk level in the corresponding grid is medium level, the corresponding area of ​​the grid in the digital twin model is marked as orange, when the highest alarm risk level in the corresponding grid is low level, the corresponding area of ​​the grid in the digital twin model is marked as yellow, and when the highest alarm risk level in the corresponding grid is no risk, the corresponding area of ​​the grid in the digital twin model is marked as blue.

[0191] The method further includes: assigning a corresponding grid administrator to each grid, the grid administrator being responsible for performing routine maintenance and alarm processing on the devices in the grid; designating grid captains for the multiple grid administrators; and assigning interactive devices to the grid captains, the interactive devices being used to exchange information with the central service platform; the grid administrators and grid captains are all trained before taking up their posts; and pre-assigning a UWB tag to each grid administrator. The method further includes:

[0192] The central service platform conducts training effectiveness tests on the corresponding grid administrators at corresponding intervals. The training effectiveness tests specifically include:

[0193] The central service platform selects a first alarm item from the preset alarm table items, and reversely generates test data based on the first alarm item; the test data is the abnormal sensor data that triggers the first alarm item.

[0194] The test data is sent to a centralized device so that the centralized device sends the test data to the corresponding intelligent sensor device. The intelligent sensor device uses the test data to replace the actually collected sensor data and displays it on the dial, so that the grid administrator can observe abnormal data through the dial of the intelligent sensor device and report the abnormal data to the grid team leader, who then reports the abnormal data to the central service platform through the interactive device.

[0195] The central service platform calculates a first matching degree between the abnormal data received from the interactive device and the test data.

[0196] The central service platform also determines the movement trajectory of each grid administrator after arriving at the target intelligent sensor device based on the UWB tag; matches the movement trajectory with a preset map to determine the monitoring locations passed by the movement trajectory; and calculates the second matching degree between each monitoring location and the preset processing behavior of the first alarm item; wherein, the target intelligent sensor device is an intelligent sensor device that displays test data.

[0197] Based on the first matching degree and the second matching degree, the training score of the grid administrator of the corresponding grid is calculated, and the training score is displayed in the digital twin model, so as to judge whether the grid administrator is suitable for the position or whether further education and training is needed through the training score.

[0198] In an optional embodiment, the central service platform further compares the sensor data with corresponding monitoring thresholds to determine whether the sensor data is abnormal. Based on the sensor data with abnormalities, the central service platform performs alarm analysis on each grid to obtain the alarms and corresponding alarm risk levels of each grid. The alarms of the downstream grids are then filtered out in combination with the alarms of the upstream grids to obtain the final alarm analysis results, which specifically include:

[0199] The monitoring thresholds include a high-level monitoring threshold, an intermediate monitoring threshold and a low-level monitoring threshold. The sensor data is compared with the high-level monitoring threshold, the intermediate monitoring threshold and the low-level monitoring threshold in turn until the comparison shows that the sensor data exceeds the range of the corresponding monitoring threshold. In this case, it is judged that the sensor data is abnormal, and the risk level of the exceeded monitoring threshold is used as the abnormality level of the sensor data.

[0200] All abnormal sensor data are used to generate an abnormal sensor array; wherein, each value in the abnormal sensor array corresponds to a sensor data, when there is no abnormality in the sensor data, the corresponding value in the abnormal sensor array is set to 0, when the abnormality level of the sensor data is low, the corresponding value in the abnormal sensor array is set to 1, when the abnormality level of the sensor data is medium, the corresponding value in the abnormal sensor array is set to 2, and when the abnormality level of the sensor data is high, the corresponding value in the abnormal sensor array is set to 3.

[0201] Use the abnormal sensor array to subtract each alarm code array in the preset alarm table item to obtain a result array. If all arrays in the result array are greater than or equal to 0, then the analysis results in the alarm corresponding to the alarm code array, and the alarm risk level of the alarm is the alarm risk level corresponding to the alarm code array; wherein, the abnormal sensor array minus the alarm code array is specifically: each numerical value in the abnormal sensor array minus the corresponding numerical value in the alarm code array.

[0202] Use all alarms to generate abnormal alarm codes, and perform AND operations on the abnormal alarm codes and the associated alarm abnormal codes corresponding to each alarm in a preset order. If the operation result is greater than 0, the alarm corresponding to the associated alarm abnormal code is filtered out.

[0203] The preset alarm table entry includes a first preset alarm table entry and a second preset alarm table entry. The first preset alarm table entry is as follows: Figure 21As shown, including sensor sequence number, sensor data, monitoring indicators, low-level monitoring threshold (including low-level monitoring threshold lower limit and low-level monitoring threshold upper limit), intermediate monitoring threshold (including intermediate monitoring threshold lower limit and intermediate monitoring threshold upper limit) and advanced monitoring threshold (including advanced monitoring threshold lower limit and advanced monitoring threshold upper limit). When the corresponding monitoring indicator only monitors the upper threshold limit or the upper threshold limit, the party that does not need to be monitored is set to the default value (such as -1).

[0204] The second preset alarm table entry is as follows Figure 26 As shown, it includes grid serial number, alarm serial number, associated alarm exception code, alarm risk level and alarm code array, wherein each alarm item corresponds to an associated alarm exception code, and corresponds to 3 alarm risk levels and 3 alarm code arrays, and each associated alarm exception code and alarm code array are obtained in advance by technical personnel in this field based on empirical analysis, specifically: according to the order of sensor serial number, the numerical value corresponding to the abnormal level (i.e. 0, 1, 2 and 3) of the sensor data that triggers the corresponding alarm is used as the corresponding numerical value in the alarm code array; the bits corresponding to other alarms that trigger the corresponding alarm are set to 1, and the other bits are set to 0 to obtain the associated alarm exception code, and each alarm item corresponds to a bit in the associated alarm exception code.

[0205] The central service platform calculates a first matching degree between the abnormal data received from the interactive device and the test data, specifically including:

[0206] The first matching degree is calculated using the first formula; the first formula is Among them, I t_n is the first matching degree obtained from the nth training effect test of the grid administrator, Num(error_data) is the number of abnormal data received by the central service platform from the interactive device, and Num(test_data) is the number of generated test data.

[0207] Calculating a second matching degree between each monitoring location and a preset processing action of the first alarm item specifically includes:

[0208] The second matching degree is calculated using the second formula; the second formula is Among them, I p_n is the second matching degree obtained from the nth training effect test of the grid administrator, Num(test_pos) is the number of first monitoring positions that the grid administrator needs to manually monitor as specified in the preset processing behavior, and Num(passed_test_pos) is the number of first monitoring positions passed by the grid administrator's movement trajectory.

[0209] Calculating the training score of the grid administrator of the corresponding grid according to the first matching degree and the second matching degree specifically includes:

[0210] The third formula is used to calculate the test results of the grid administrator's training effectiveness test.

[0211] Use the fourth formula to calculate the training score of the grid administrator after this training effect test.

[0212] The third formula is I' t_n =k1I t_n +k2I p_n +k3I' t_n-1 , the fourth formula is I g_n =k4I' t_n +k5I r ; k1, k2, k3, k4 and k5 are corresponding weight values, where k1+k2+k3=1, k4+k5=1, I′ t_n is the test result of the nth training effect test, I r is the evaluation score of the grid administrator after training, I g_n is the training score of the grid administrator after the nth training effect test, I t_n is the first matching degree obtained from the nth training effect test of the grid administrator, I p_n is the second matching degree obtained from the nth training effect test of the grid administrator, I′ t_n-1 This is the test result of the n-1th training effectiveness test for grid administrators.

[0213] The methods described in Example 1 are applicable to this embodiment and will not be described in detail here.

[0214] Example 5:

[0215] like Figure 27 The figure is a schematic diagram of the architecture of the metal smelting enterprise regional grid dynamic risk classification control device according to an embodiment of the present invention. The metal smelting enterprise regional grid dynamic risk classification control device according to this embodiment includes one or more processors 21 and a memory 22. Figure 27 A processor 21 is taken as an example.

[0216] The processor 21 and the memory 22 may be connected via a bus or other means. Figure 27 The bus connection is taken as an example.

[0217] Memory 22, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs and non-volatile computer executable programs, such as the method for regional grid-based dynamic risk hierarchical management and control of metal smelting enterprises described in any one of Examples 1 to 4. Processor 21 executes the method for regional grid-based dynamic risk hierarchical management and control of metal smelting enterprises by running the non-volatile software program and instructions stored in memory 22.

[0218] The memory 22 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory 22 may optionally include a memory remotely located relative to the processor 21, and such remote memory may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0219] The program instructions / modules are stored in the memory 22, and when executed by the one or more processors 21, the method for regional grid-based dynamic risk classification control of metal smelting enterprises described in any one of the above-mentioned embodiments 1 to 4 is executed.

[0220] It is worth noting that the information interaction, execution process, etc. between the modules and units within the above-mentioned devices and systems are based on the same concept as the processing method embodiment of the present invention. The specific content can be found in the description of the method embodiment of the present invention and will not be repeated here.

[0221] Those skilled in the art will understand that all or part of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a disk or an optical disk, etc.

[0222] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A regional grid-based dynamic risk classification management and control method for metal smelting enterprises, characterized in that: A central service platform is set up for the enterprise area, the enterprise area is divided into multiple grids, an intelligent sensor device is set up at each monitoring location of each grid, and a centralized device is set up for one or more grids, the centralized device is used to collect sensor data from the intelligent sensor device, and report the collected sensor data to the central service platform; the method includes: The central service platform compares the sensor data with the corresponding monitoring threshold to determine whether there is any abnormality in the sensor data; First, for each grid, the abnormal sensor data in the corresponding grid is analyzed to obtain the alarm of each grid. Then, combined with the alarm of the upstream grid, the alarm of the downstream grid is filtered out to obtain the final alarm analysis result; The method first analyzes the abnormal sensor data in each grid to obtain the alarm of each grid, and then filters the alarm of the downstream grid in combination with the alarm of the upstream grid to obtain the final alarm analysis result, which specifically includes: Using all abnormal sensor data, an abnormal sensor code is generated; wherein each bit in the abnormal sensor code corresponds to one sensor data, and when the corresponding sensor data is abnormal, the corresponding bit in the abnormal sensor code is set to 1, otherwise, the corresponding bit is set to 0, thereby forming the abnormal sensor code; Performing an AND operation on the abnormality sensing code and each alarm code in the preset alarm table to obtain an operation result. If the operation result is greater than 0, analyzing and obtaining the alarm corresponding to the alarm code; Use all alarms to generate abnormal alarm codes, perform AND operations on the abnormal alarm codes and the associated alarm abnormal codes corresponding to each alarm in a preset order, and if the operation result is greater than 0, filter out the alarm corresponding to the associated alarm abnormal code; Among them, the preset alarm table items include the monitoring threshold corresponding to each sensor data, the alarm code of each alarm item and the associated alarm exception code of each alarm item; among them, in the alarm code of the corresponding alarm item, the bits corresponding to all the sensor data that cause the alarm item to be abnormal are 1, and the other bits are 0; in the associated alarm exception code of the corresponding alarm item, the bits corresponding to all other alarms that cause the alarm are 1, and the other bits are 0.

2. The method for regional grid-based dynamic risk classification management and control of metal smelting enterprises according to claim 1 is characterized in that: Each grid is assigned a corresponding grid administrator, who is responsible for performing daily maintenance and alarm processing on the equipment in the grid. Grid captains are also designated for multiple grid administrators and assigned interactive devices for interacting with the central service platform. Both grid administrators and grid captains undergo education and training before taking up their posts. The method also includes: The central service platform conducts training effectiveness tests on the corresponding grid administrators at corresponding intervals. The training effectiveness tests specifically include: The central service platform selects a first alarm item from the preset alarm table items, and reversely generates test data based on the first alarm item; the test data is the sensor data that causes the first alarm item to be abnormal; The test data is sent to a centralized device so that the centralized device sends the test data to the corresponding intelligent sensor device, and the intelligent sensor device uses the test data instead of the actually collected sensor data to display on the dial, so that the grid administrator can observe abnormal data through the dial of the intelligent sensor device and report the abnormal data to the grid leader, who then reports the abnormal data to the central service platform through the interactive device; The central service platform calculates the matching degree between the abnormal data received from the interactive device and the test data; Based on the matching degree, the training score of the grid administrator of the corresponding grid is calculated and displayed on the central service platform, so as to judge whether the grid administrator is suitable for the position or whether further education and training is needed through the training score.

3. The method for regional grid-based dynamic risk classification management and control of metal smelting enterprises according to claim 2 is characterized in that: The first alarm item is an alarm item whose alarm processing priority is lower than the preset priority and whose alarm level is higher than the preset level.

4. The method for regional grid-based dynamic risk classification management and control of metal smelting enterprises according to claim 2 is characterized in that: The central service platform calculates the matching degree between the abnormal data received from the interactive device and the test data, specifically including: The matching degree is calculated using the first formula; the first formula is ;in, is the matching degree obtained from the nth training effect test of the grid administrator, The number of abnormal data received by the central service platform from interactive devices. is the number of test data generated.

5. The method for regional grid-based dynamic risk classification management and control of metal smelting enterprises according to claim 2 is characterized in that: Calculating the training score of the grid administrator of the corresponding grid according to the matching degree specifically includes: Use the second formula to calculate the test results of the grid administrator's training effectiveness test; Use the third formula to calculate the training score of the grid administrator after the training effect test; The second formula is , the third formula is ; 、 、 and are the corresponding weight values, among which, , , is the test result of the nth training effect test, is the evaluation score obtained by the grid administrator after training, is the training score of the grid administrator after the nth training effect test, is the matching degree obtained from the nth training effect test of the grid administrator, This is the test result of the n-1th training effectiveness test for grid administrators.

6. The method for regional grid-based dynamic risk classification management and control of metal smelting enterprises according to claim 2 is characterized in that: During the training effect test, the central service platform still receives sensor data. When it is analyzed that an alarm occurs in the grid where the training effect test is being conducted, the training effect test is terminated to display the sensor data collected by the intelligent sensor equipment on the dial. The grid captain is informed of the time of the training effect test through the interactive device so that the grid captain can notify the grid administrator to terminate the training effect test and thus handle the alarm in time.

7. The method for regional grid-based dynamic risk classification management and control of metal smelting enterprises according to claim 2 is characterized in that: The central service platform conducts training effectiveness tests on the corresponding grid administrators at corresponding intervals, specifically including: At intervals of a first preset period, a random number is generated for the grid. If the generated random number is a prime number, statistics are then generated to determine whether device configuration information of the grid has changed within a second preset period before the current moment, or whether an alarm has been generated by the grid within the second preset period before the current moment; If the device configuration information of the grid has not changed within the second preset period before the current moment, and the grid has not generated an alarm, then a training effectiveness test is conducted on the grid administrator of the grid; The generating of random numbers specifically includes: A seed number is obtained by adding the grid code to the current time, a random intermediate number is generated using the seed number, and a modulo M is obtained using the random intermediate number to obtain the random number; in, , is the default value, is the third preset period, is the number of alarms in the grid within the third preset period. If no alarm occurs in the grid within the third preset period before the current moment, .

8. A non-volatile computer storage medium, characterized in that The computer storage medium stores computer-executable instructions, which are executed by one or more processors to complete the regional grid-based dynamic risk classification management and control method for metal smelting enterprises as described in any one of claims 1-7.

9. A regional grid-based dynamic risk classification control device for metal smelting enterprises, characterized in that: The device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the method for regional grid-based dynamic risk grading control of metal smelting enterprises as described in any one of claims 1-7.

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