Method, device and monitoring system for personnel dispatching in emergency scenarios

Abstract

Disclosed are a personnel dispatching method, device and monitoring system in an emergency scenario. A personnel dispatching method in an emergency scenario is applied to an industrial site comprising a plurality of devices and a data collector collecting data of the devices. The method comprises: receiving device identification data and device attribute data from the data collector, and identifying an emergency scenario based on the received data; and determining personnel to be dispatched based on the identified emergency scenario, wherein identifying the emergency scenario based on the received data comprises: identifying the emergency scenario according to a pre-established first undirected graph comprising a plurality of first nodes and a plurality of second nodes and a plurality of edges, wherein the first nodes represent preset emergency scenarios, the second nodes represent the device identification data and the device attribute data, and the edges connect the second nodes to the associated first nodes.

Description

Technical Field

[0001] The present disclosure relates to a personnel dispatching method, electronic device, computer-readable storage medium, device, and monitoring system in an emergency scenario. Background Art

[0002] A data center generally refers to the critical equipment and the infrastructure required to operate this critical equipment within a physical space, enabling centralized processing, storage, transmission, exchange, and management of information. This infrastructure includes key physical infrastructure such as building facilities, the environment, power supply systems, cooling systems, cabinet systems, fire protection systems, and monitoring systems.

[0003] In the event of an emergency scenario such as a major accident or dangerous problem in a data center, emergency response measures need to be taken in a timely manner to control the deterioration of the situation and reduce the destructiveness. Summary of the Invention

[0004] The present disclosure provides a personnel dispatching method and device in an emergency scenario.

[0005] According to some embodiments of the present disclosure, a personnel scheduling method for emergency scenarios is provided, which is applied to an industrial site including multiple devices and a data collector for collecting data from each device, the method including: receiving device identification data and device attribute data from the data collector, and identifying the emergency scenario based on the received data; and determining personnel to be dispatched based on the identified emergency scenario, wherein identifying the emergency scenario based on the received data includes: identifying the emergency scenario based on a pre-established first undirected graph including multiple first nodes, multiple second nodes and multiple edges, wherein the first node represents a preset emergency scenario, the second node represents the device identification data and device attribute data, and the edge connects the second node with the associated first node.

[0006] Other features, aspects and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The preferred embodiments of the present disclosure are described below with reference to the accompanying drawings. The drawings described herein are used to provide a further understanding of the present disclosure. Each of the drawings, together with the following detailed description, is included in this specification and forms a part of the specification to explain the present disclosure. It should be understood that the drawings described below only relate to some embodiments of the present disclosure and do not constitute a limitation of the present disclosure. In the drawings:

[0008] Figure 1 is a block diagram of an exemplary device for dispatching personnel in response to an emergency scenario (also referred to as an emergency response device) according to some embodiments of the present disclosure.

[0009] Figure 2 is a flow chart illustrating an exemplary method for dispatching personnel in response to an emergency scenario (also referred to as an emergency handling method) according to some embodiments of the present disclosure.

[0010] Figure 3 is a flowchart illustrating an exemplary process of identifying emergency scenarios based on device identification data and device attribute data according to some embodiments of the present disclosure.

[0011] Figure 4 The figure illustrates an exemplary first undirected graph used in identifying emergency scenarios according to some embodiments of the present disclosure.

[0012] Figure 5 is a flow chart illustrating an exemplary process for determining personnel to dispatch based on an identified emergency scenario according to some embodiments of the present disclosure.

[0013] Figure 6 The figure illustrates an exemplary second undirected graph used in determining personnel to dispatch according to some embodiments of the present disclosure.

[0014] Figure 7 The diagram illustrates a general hardware environment in which the present disclosure may be applied according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0015] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. However, it is obvious that the embodiments described are only some embodiments of the present disclosure, rather than all embodiments. The following description of the embodiments is actually only illustrative and is in no way intended to limit the present disclosure and its application or use. It should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein.

[0016] It should be understood that the various steps described in the method embodiments of the present disclosure can be performed in different orders and / or performed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect. Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments should be interpreted as being merely exemplary and do not limit the scope of the present disclosure.

[0017] In the present disclosure, a data center includes multiple devices and a data collector that collects device attribute data representing the properties of each device. The data collector outputs device identification data and device attribute data. Here, the device identification data uniquely identifies the device, and the device attribute data includes the device's electrical attribute data and / or the device's HVAC attribute data. A data center may, for example, include computer rooms and cabinets. More specifically, a data center may include devices such as AC main cabinets, high-voltage DC main cabinets, and busbars. The data collector may collect device electrical attribute data, such as the main switch status data for AC main cabinets, high-voltage DC main cabinets, and busbars. For another example, a data center may include devices such as air conditioners and multiple temperature and humidity sensors within the same modular computer room. The data collector may collect device HVAC attribute data, such as the air supply temperature of the air conditioner and the temperature and humidity within the modular computer room. For another example, a data center may include devices such as cooling water tanks. The data collector may collect device HVAC attribute data, such as the cooling water tank level. By monitoring and analyzing device attribute data of multiple devices in a data center, possible emergency scenarios in the data center can be automatically identified.

[0018] It should be understood that the emergency response method and device disclosed herein are not limited to monitoring data centers, but can also be used to monitor other industrial sites with multiple devices and multiple data collectors, such as production workshops, etc.

[0019] Figure 1 is a block diagram of an exemplary emergency response device 100 according to some embodiments of the present disclosure.

[0020] like Figure 1 As shown, the device 100 may include: a scenario design component 110, configured to design an emergency scenario; a scenario recognition component 120, configured to identify the emergency scenario based on device identification data and device attribute data; a personnel determination component 130, configured to determine the personnel to be dispatched based on the identified emergency scenario; and a notification and display component 140, configured to send a notification to the determined personnel to be dispatched and display the position movement of the determined personnel to be dispatched.

[0021] The following will further describe in detail Figure 1 The operation of each component is shown.

[0022] Figure 2 is a flow chart illustrating an exemplary emergency handling method 200 according to some embodiments of the present disclosure.

[0023] The method 200 starts from step S210 . At step S210 , the scenario design component 110 designs an emergency scenario.

[0024] Specifically, the scenario design component 110 defines the number and name of the emergency scenario, defines the equipment, equipment attributes, corresponding thresholds and corresponding weights involved in the scenario, and also defines the personal skills required to handle the scenario and the required personnel's proficiency in the personal skills.

[0025] In some embodiments, component 110 defines an emergency scenario as follows:

[0026] Scene = {code (number), name (name), device_code (device identifier), attribute_code (device attribute), upper (threshold), weight (weight), C = [{c1, v1}, {c2, v2}, {c3, v3}, ...]}.

[0027] Here, Scene represents an exemplary scenario defined. The scenario has a number and name that uniquely identify the scenario, device_code represents the device identifier (i.e., device identification data), attribute_code represents the device attribute, upper represents the threshold value corresponding to the device attribute of the device, weight represents the weight corresponding to the device attribute of the device, C represents the multiple personal skills required to handle the scenario and the required personnel's proficiency in each personal skill, {c i ,v i} indicates the required personal skills c i and the skills required for that individual i Proficiency level i , where i is an integer greater than or equal to 1.

[0028] It should be understood that component 110 can similarly define many scenarios, such as hundreds or thousands of scenarios.

[0029] It should be understood that a scenario can be associated with one or more sets of device identifiers, device attributes, thresholds, and weights. Table 1 below shows an example of a scenario associated with multiple sets of device identifiers, device attributes, thresholds, and weights.

[0030] Table 1

[0031]

[0032] As shown in Table 1, the emergency scenario "power supply interruption at the cabinet level" involves, for example, three devices: "AC cabinet", "HVDC cabinet", and "busbar". More specifically, for each device, such as "AC cabinet", the device attribute involved is "main switch status", the threshold is "open", and the weight is 3. That is, when the main switch status of the AC cabinet is open, this indicates that there may be an emergency scenario of "power supply interruption at the cabinet level". In addition, when the main switch status of the AC cabinet and the HVDC cabinet are both open, this indicates that there is a high probability of an emergency scenario of "power supply interruption at the cabinet level". In addition, when the main switch status of the AC cabinet, the HVDC cabinet, and the busbar are all open, this indicates that there is a high probability of an emergency scenario of "power supply interruption at the cabinet level". The information in the "Location" column in Table 1 can be regarded as part of the device identification, which clearly indicates the location of the listed devices: "301 module room" and the cabinet number "3a4B".

[0033] In this disclosure, device identification data may include any data that can uniquely identify a device, such as the device number, device name, and device location. In this disclosure, device attributes refer to device parameters (or data identifying such parameters), such as the air supply temperature of an air conditioner, while device attribute data refers to the device parameters and their specific values, such as the air supply temperature of an air conditioner being 25°C.

[0034] It should be understood that the association between the emergency scenario and one or more groups of device identifiers, device attributes, thresholds, and weights involved, as well as the association between the emergency scenario and the personal skills and proficiency required to handle the scenario, can be determined based on expert experience. Alternatively, some of these associations can be determined based on expert experience, while others can be determined using machine learning techniques. Alternatively, these associations can be determined using machine learning techniques. Any known machine learning technique can be used, and this disclosure does not limit this.

[0035] Next, method 200 proceeds to step S220, where the scene recognition component 120 receives the device identification data and the device attribute data from the data collector and identifies the emergency scenario based on the received data. More specifically, the scene recognition component 120 identifies the emergency scenario using a pre-established first undirected graph comprising a plurality of first nodes, a plurality of second nodes, and a plurality of edges, wherein the first nodes represent the preset emergency scenarios, the second nodes represent the device identification data and the device attribute data, and the edges connect the second nodes to the associated first nodes.

[0036] Figure 3 is a flowchart illustrating a specific procedure of the scene recognition process 300 at step S220 . Figure 4 The figure shows an exemplary first undirected graph.

[0037] like Figure 4 As shown, black nodes correspond to first nodes, and white nodes correspond to second nodes. The scene recognition component 120 can form a graph based on the scene designed in step S210. Specifically, each first node corresponds to a scene defined in step S210, and each second node corresponds to a device identifier and device attribute defined in step S210. Multiple edges connect the first node representing a scene with the second nodes representing the devices and device attributes involved in the scene.

[0038] exist Figure 4 In FIG, the first nodes 410 and 411 represent two preset emergency scenarios. The second nodes 420-425 represent six sets of device identification data and device attribute data. Figure 4 As shown in the figure, the first node 410 is connected to five second nodes 420, 421, 423, 424, and 425, which indicates that the data represented by these five second nodes are associated with the scene represented by the first node. In addition, the first node 411 is connected to three second nodes 421, 422, and 425, which indicates that the data represented by these three second nodes are associated with the scene represented by the first node. It should be understood that the number of the first node or the second node is not limited to the number shown in the figure. In practice, the number of the first node or the second node can be, for example, hundreds or thousands.

[0039] exist Figure 4 In the first undirected graph, each edge is associated with a threshold value and a weight w. The threshold value and weight here are the threshold value and weight set for the scenario in step S210, corresponding to the device identification and the device attribute.

[0040] exist Figure 4 Also shown is a hash table for associating device identification data and device attribute data from the data collector with corresponding second nodes. This hash table uses "device identification # device attribute" as a key value and maps this key value to the corresponding second node. It should be understood that associating data from the data collector with the corresponding second node is not limited to using a hash table; other known methods can be used to achieve this association.

[0041] Next, refer to Figure 3 The scene recognition process 300 at step S220 may be performed by the scene recognition component 120 .

[0042] In step S2202 , the component 120 identifies an edge among the edges of the first undirected graph, where the device attribute data represented by the corresponding second node and the corresponding threshold satisfy a preset condition, as a target edge.

[0043] Specifically, for each edge of the first undirected graph, it is determined in turn whether the device attribute data of the second node connected to the edge and the threshold associated with the edge meet the preset conditions. If the preset conditions are met, the edge is identified as a target edge.

[0044] The preset condition may, for example, be that the device attribute data of the second node connected to the edge is greater than or equal to the threshold associated with the edge (for example, the air supply temperature of the air conditioner is greater than or equal to the preset temperature). Alternatively, the preset condition may, for example, be that the device attribute data of the second node connected to the edge is less than or equal to the threshold associated with the edge (for example, the liquid level of the water tank is less than or equal to the preset value). Alternatively, the preset condition may, for example, be that the device attribute data of the second node connected to the edge is the threshold (for example, the value of the main switch state of the AC power cabinet indicates that the switch is open). It should be understood that the preset condition can be set according to actual needs.

[0045] like Figure 4 As shown in , taking the second node 425 as an example, assuming that the device attribute data represented by the second node 425 is greater than or equal to the threshold value 1.3 and the threshold value 0.8 of the two edges connected to it, then the two edges connected to the second node 425 are both target edges. On the contrary, assuming that the device attribute data represented by the second node 425 is less than the threshold value 1.3 and the threshold value 0.8 of the two edges connected to it, then the two edges connected to the second node 425 are not target edges. It should be understood that in Figure 4 In the embodiment, a single threshold value is set for each edge, however, two or more threshold values ​​may be set for each edge as needed. By using the threshold value, device attribute data falling within the range defined by the threshold value can be identified.

[0046] In step S2204, the component 120 determines the score of the first node based on the corresponding weight of the target edge connected to the first node.

[0047] In some embodiments, component 120 determines the sum of the corresponding weights of the target edges connected to the first node as the score of the first node.

[0048] Still refer to Figure 4 In the first undirected graph shown in Figure 4If all edges in are target edges, then the sum of the weights corresponding to the target edges connected to the first node 410 is: 2+1+2+5+1=11, and the sum of the weights corresponding to the target edges connected to the first node 411 is: 2+1+3=6. In other words, the score for the first node 410 is 11, and the score for the first node 411 is 6. It should be understood that the method for determining the score is not limited to calculating the sum of weights, and the score for the first node can be determined in other ways based on the weights. It should be understood that the score of the first node can reflect the likelihood of the emergency scenario it represents occurring.

[0049] In step S2206 , the component 120 sorts the plurality of first nodes according to the scores of the first nodes, and determines one or more possible emergency scenarios as the identified emergency scenarios based on the sorting result.

[0050] In the first undirected graph, assuming that there are M first nodes (M is an integer greater than or equal to 1), the M first nodes are sorted according to their scores, and the scenarios corresponding to the top N first nodes with the highest scores (N is an integer greater than or equal to 1, N≤M) can be determined as possible emergency scenarios. Incidentally, first nodes with a score of zero (i.e., no target edge connected to them) do not need to be sorted.

[0051] It is understood that the one or more identified emergency scenarios output by the scene recognition component 120 can be directly output to the personnel determination component 130. Alternatively, the one or more identified emergency scenarios output by the scene recognition component 120 can also be output to the emergency response specialist for reference, and the identified scenarios confirmed by the emergency response specialist are output to the personnel determination component 130.

[0052] A specific example of scene recognition processing is described below. This example includes steps 1 to 4 as described below. The scene recognition processing can be performed by the scene recognition component 120.

[0053] Step 1: Initialize the scene memory structure, denoted as graph G scene and hash table H condition This step 1 can be subdivided into the following steps 1.0-1.3.

[0054] Step 1.0, initialize graph G scene and hash table H condition .

[0055] Step 1.1: According to the number and name of the emergency scenario designed in the above step S210, the first node N root Insert into Figure G scene , denoted as N root={scene_code, scene_name, ...} Here, the number of the first nodes corresponds to the number of emergency scenes designed in the aforementioned step S210.

[0056] Step 1.2: The second node N is configured according to the device identification and device attributes involved in the emergency scenario designed in the aforementioned step S210. condition Insert into Figure G scene , denoted as N condition ={device_code,attribute_code,……}. Use the character “#” to concatenate the device identifiers and device attributes involved into a string, and use this string as the hash table H condition The key value is mapped to the graph G scene The second node N in condition The memory address of .

[0057] Step 1.3: Based on the relationship between the emergency scenario designed in the aforementioned step S210 and the device identification and device attributes involved, scene Insert the first node N in the connection root and the second node N condition And, for each edge, an associated threshold upper and weight w are set, where the threshold upper and weight w are the threshold and weight designed in the aforementioned step S210.

[0058] Step 2: Collect the attribute data of the device, denoted as R device =[{cc1,vv1},{cc2,vv2},{cc3,vv3},……]. Here, cc i Indicates device identification data, vv i Represents device attribute data, i is an integer greater than or equal to 1. It should be understood that in some embodiments, the same device may have multiple attribute data, in which case these multiple attribute data may correspond to multiple second nodes N condition .

[0059] Step 3, obtain R device Records in , search graph G scene The first node N in root , denoted as R spare =[{N, W, D, A}], where N represents the first node N root In the scenario represented, W represents weight, D represents device identification data, and A represents device attribute data. Step 3 can be further divided into the following steps 3.0-3.3.

[0060] Step 3.0, get R device A record in {cci ,vv i}.

[0061] Step 3.1, retrieve the hash table H according to "device identification # device attributes" condition The key value in , so as to find the corresponding second node N condition .

[0062] Step 3.2, compare the threshold corresponding to the edge of the second node found with vv i If the conditions are met, the first node N is retrieved along the corresponding edge (i.e. the target edge). root , denoted as N; and the weight w of the edge that meets the conditions is denoted as W.

[0063] Step 3.3, put {N, W, D, A} into array R spare ; and return to step 3.0-3.2 until R device Until all the data in have been processed.

[0064] It should be understood that when there are multiple target edges connected to a first node N root When the first node N root The weight W is the value obtained by summing the corresponding weights of these multiple target edges.

[0065] Step 4, put R spare After sorting by W, the output is made. Alternatively, a predetermined number of Rs ranked first can be output. spare .

[0066] Next, the method 200 proceeds to step S230, where the personnel determination component 130 determines personnel to be dispatched based on the emergency scenario identified in step S220. More specifically, the personnel determination component 130 determines the personnel to be dispatched using a pre-established second undirected graph comprising a plurality of third nodes, a plurality of fourth nodes, and a plurality of edges, wherein the third nodes represent personnel available for dispatch, the fourth nodes represent personal skills, and the edges connect the fourth nodes with associated third nodes, and wherein in the second undirected graph, each edge is associated with a weight, which represents the proficiency of the personnel connected by the edge in the personal skills connected by the edge.

[0067] Figure 5 2 is a flowchart illustrating a detailed procedure of the personnel determination process 500 at step S230 . Figure 6 The figure shows an exemplary second undirected graph.

[0068] like Figure 6As shown, the black nodes correspond to the third nodes and the white nodes correspond to the fourth nodes. Each third node can represent a person available for dispatch, such as an electrical engineer, HVAC engineer, etc. Each fourth node can represent the personal skills of the associated person. The personal skills of a person can be, for example, the qualification certificate held by the person. The edge connecting the third and fourth nodes indicates that the person represented by the connected third node has the personal skills represented by the connected fourth node. Figure 6 As shown, each edge is associated with a weight w', which represents the proficiency of the person connected by the edge for the personal skills connected by the edge. The proficiency here indicates the experience of the person in handling business related to his or her personal skills (such as his or her qualification certificate).

[0069] exist Figure 6 In the second undirected graph, the third node 610 is connected to five fourth nodes 620, 621, 622, 623, and 624, which indicates that the person represented by the third node 610 has five personal skills represented by these five fourth nodes. Similarly, the third node 611 is connected to three fourth nodes 623, 625, and 626, which indicates that the person represented by the third node 611 has three personal skills represented by these three fourth nodes. Moreover, taking the edge connecting the third node 610 and the fourth node 620 as an example, the edge has a weight of 2, which indicates that the proficiency of the person represented by the third node 610 in the personal skill represented by the fourth node 620 is 2. Figure 6 In the exemplary diagram of FIG, a numerical value between 0 and 5 is used to represent the proficiency of a person in mastering a personal skill. It should be understood that the number of third nodes or fourth nodes is not limited to the number shown in the diagram. In practice, the number of third nodes or fourth nodes can be, for example, hundreds or thousands.

[0070] It should be understood that the second undirected graph can be pre-established based on a personnel information database. The personnel information database may include: personnel identification information (such as name, ID number, etc.), personnel personal skill information (such as which qualifications they hold), and personnel proficiency information (such as a numerical value of 0 representing no experience and a numerical value of 5 representing very rich experience). The second undirected graph can be pre-established by the personnel determination component 130 based on the personnel information database.

[0071] Next, refer to Figure 5Next, we will introduce the personnel determination process 500 at step S230. The personnel determination process 500 may include the following steps S2302-S2308. If multiple emergency scenarios are identified at step S220, the following steps S2302-S2308 may be performed for each of the multiple emergency scenarios. Furthermore, a predetermined number of personnel to be dispatched may be determined for each emergency scenario in turn. The personnel determination process 500 may be performed by the personnel determination component 130.

[0072] In step S2302, component 130 determines the personal skills and proficiency required to handle the emergency scenario based on the identified emergency scenario.

[0073] Specifically, component 130 can determine the personal skills and proficiency required to handle a certain emergency scenario based on the emergency scenario designed in step S210 (see the definition of emergency scenario in component 110). More specifically, for a specific emergency scenario, the required personal skills and proficiency C = [{c1, v1}, {c2, v2}, {c3, v3}, ...] associated therewith can be determined. Here, c i Represents the required personal skills, v i Represents the required proficiency level, i is an integer greater than or equal to 1.

[0074] In step S2304, the component 130 determines, for each target fourth node representing the required personal skill, the difference between the proficiency level represented by the corresponding weight and the required proficiency level, as well as the absolute value of the difference.

[0075] refer to Figure 6 In the second undirected graph shown in FIG, assuming that fourth nodes 622, 623, and 624 represent required individual skills c1, c2, and c3, respectively, then these three fourth nodes are the target fourth nodes. For each of the target fourth nodes 622, 623, and 624, the difference between the individual's proficiency and the required proficiency, as well as the absolute value of this difference, is determined. Assuming that the required proficiency v1, v2, and v3 are all 2, then for the target fourth nodes 622, 623, and 624, the following four differences are determined: 1.6 - 2 = -0.4; 4.5 - 2 = 2.5; 3.5 - 2 = 1.5; and 1.8 - 2 = -0.2. The absolute values ​​of these four differences can be determined accordingly.

[0076] In step S2306 , the component 130 determines, for a third node connected to one or more target fourth nodes, a sum of the difference values ​​corresponding to the connected target fourth nodes and a sum of the absolute values ​​of the difference values.

[0077] Still refer to Figure 6 In the second undirected graph shown in FIG, for a third node 610 connected to three target fourth nodes, the sum of the differences corresponding to the three connected target fourth nodes can be determined to be: -0.4 + 2.5 + 1.5 = 3.6, and the sum of the absolute values ​​of the differences can be determined to be: 0.4 + 2.5 + 1.5 = 4.4. In addition, for a third node 611 connected to one target fourth node, the sum of the differences corresponding to the connected target fourth node can be determined to be: -0.2, and the sum of the absolute values ​​of the differences can be determined to be: 0.2.

[0078] In step S2308, the component 130 sorts the plurality of third nodes based on the sum of the differences and the sum of the absolute values ​​of the differences, and determines personnel to be dispatched based on the sorting result.

[0079] Still refer to Figure 6 In the second undirected graph shown in , considering the sum of the aforementioned differences and the sum of the absolute values ​​of the aforementioned differences of the third node 610, i.e., 3.6 and 4.4, and considering the sum of the aforementioned differences and the sum of the absolute values ​​of the aforementioned differences of the third node 611, i.e., -0.2 and 0.2, the third nodes can be sorted.

[0080] In some embodiments, the third nodes are first sorted in descending order of the sum of the aforementioned differences. Secondly, if the sum of the aforementioned differences is equal, the smaller the sum of the absolute values ​​of the aforementioned differences, the higher the ranking. Based on this sorting rule, it can be determined that the sorting of the third nodes 610 and 611 is such that 610 is ranked before 611. It will be appreciated that by using this sorting rule, individuals whose personal skills and proficiency are closer to the required personal skills and proficiency can be selected. It should be understood that this sorting rule is merely exemplary and the present disclosure is not limited thereto. Other possible sorting rules may be employed.

[0081] Based on the sorting result, the component 130 may select a predetermined number of people ranked high as the people to be dispatched. Incidentally, for a third node that has no target fourth node connected to it, it may not be sorted.

[0082] In some embodiments, when determining the personnel to be dispatched, the personal skills of the one or more personnel to be dispatched can be made to cover all required personal skills, or cover as many required personal skills as possible.

[0083] In other embodiments, when determining the personnel to be dispatched, the personnel to be dispatched may be determined based on the personnel's distance from the emergency scene. For example, the order determined in step S2308 may be modified based on the personnel's distance from the emergency scene, so that personnel closer to the emergency scene are placed higher in the order.

[0084] In other embodiments, when determining the personnel to be dispatched, the personnel to be dispatched may also be determined based on the personnel's tool readiness. For example, the order determined in step S2308 may be modified based on the personnel's tool readiness, so that personnel with ready tools are placed higher in the order. Here, tools refer to the tools required to handle the identified emergency scenario.

[0085] It is understood that the one or more determined personnel output by the personnel determination component 130 can be directly output to the notification and display component 140. Alternatively, the one or more determined personnel output by the personnel determination component 130 can also be output to the emergency response specialist for reference, and the determined personnel confirmed by the emergency response specialist can be output to the notification and display component 140.

[0086] A specific example of personnel determination processing is described below. This example includes steps 1 to 3 as described below. The personnel determination processing can be executed by the personnel determination component 120.

[0087] Step 1: Initialize the memory structure of personnel and skills.

[0088] Furthermore, step 1 can be further divided into the following steps 1.0-1.3.

[0089] Step 1.0, initialize graph G staff .

[0090] Step 1.1: insert the third node N according to the personnel identification information in the personnel information database staff Go to Figure G staff , denoted as S staff ={staff_code,staff_name,……}.

[0091] Step 1.2: insert the fourth node N according to the personal skill information of the personnel in the personnel information database. certificate to G staff , denoted as C certificate ={certificate_code,……}.

[0092] Step 1.3: Based on the relationship between personnel identification information and personal skill information in the personnel information database, staff Insert connection N staff and N certificate The undirected edge of , sets the edge weight to the individual skill proficiency, denoted as w'.

[0093] Step 2, according to the R spare Find the right person and put it in the array, denoted as R spare_staff=[{{N, W, D, A}, staff_code, {d1, d2}, ...}, ...] Here, d1 represents the sum of the differences as described above, and d2 represents the sum of the absolute values ​​of the differences as described above.

[0094] Furthermore, step 2 can be further divided into the following steps 2.0-2.3.

[0095] Step 2.0, obtain R as described above spare A record in is denoted as {N, W, D, A}.

[0096] Step 2.1: Obtain the skill list required to handle the scenario according to scenario N, denoted as C = [{c1, v1}, {c2, v2}, {c3, v3}, ...].

[0097] Step 2.2, get the data in C in sequence, denoted as {c i , v i}. Find c i (corresponding to the fourth target node) to N staff Edges, calculate w' and v in the path i The difference is recorded as d1, and the absolute value of the difference is recorded as d2. Insert {{N,W,D,A},staff_code,{d1,d2}} into the array R spare_staff .

[0098] Step 2.3, jump to step 2.0, and repeat steps 2.0-2.2 until R spare until all records have been processed.

[0099] It should be understood that when multiple target fourth nodes N certificate Connected to a third node n staff When , the sum of the difference values ​​corresponding to the connected plurality of target fourth nodes is calculated as d1, and the sum of the absolute values ​​of the difference values ​​is calculated as d2.

[0100] Step 3.0: According to the scenario N, R spare_staff After sorting, R is sorted based on d1 and d2. spare_staff Sort. At this time R spare_staff Alternatively, the top predetermined number of R in each scene N can be output. spare_staff .

[0101] Next, the method 200 proceeds to step S240 , where the notification and display component 140 sends a notification to the determined personnel to be dispatched and displays the position movement of the personnel to be dispatched.

[0102] Specifically, the notification and display component 140 can notify the identified personnel to be dispatched of their arrival at the emergency scene and can also notify the identified personnel of the operational instructions for handling the emergency scene. This notification can be via phone, email, text message, or the like. The operational instructions for handling the emergency scene can be, for example, an electronic document such as a Word document, PDF document, or TXT document that outlines the emergency response steps. Furthermore, component 140 can also notify the emergency response specialist of the alert message. Furthermore, the notification and display component 140 can highlight the location of the emergency scene and the location of the identified personnel to be dispatched on a map, and also highlight the movement of the identified personnel to be dispatched on the map. For example, a map can be displayed on the terminal device of the emergency response specialist and / or the terminal device of the dispatched personnel, with the location of the emergency scene and the location of the dispatched personnel displayed on the map, such as by color, flashing, or the like, and the movement of the dispatched personnel displayed on the map, such as by color, flashing, or the like. This display can be achieved using known location tracking technology, and this disclosure is not limited thereto.

[0103] The above reference Figure 2-Figure 6 The emergency response method 200 of the present disclosure is introduced. It should be understood that Figure 2 Steps S210 and S240 are not required. For example, a pre-designed set of emergency scenarios can be used to perform the scene recognition and personnel identification processes described above, without the need to perform the design steps shown in step S210. For another example, the notification and display steps shown in step S240 can be performed by a device other than the emergency response device 100.

[0104] In some embodiments, the present disclosure further provides a monitoring system for an industrial site, the monitoring system comprising an electronic device including a memory and a processor coupled to the memory, the memory storing instructions that, when executed by the processor, cause the electronic device to execute the emergency response method of the present disclosure. For example, the emergency response method of the present disclosure can be executed by one or more electronic devices (such as computers) in the monitoring system of the industrial site.

[0105] By using the emergency response method and equipment disclosed herein, it is possible to automatically monitor the computing center, promptly and accurately detect possible emergency scenarios, and promptly and accurately determine the personnel to be dispatched. It is meaningful to detect emergency scenarios as early as possible and to assign personnel to handle them as early as possible, which makes it possible to effectively handle emergency scenarios. In addition, by using algorithms to identify emergency scenarios and determine personnel, it is possible to assist managers in making accurate decisions, avoiding managers making incorrect judgments and decisions due to lack of experience or poor consideration.

[0106] Hardware Implementation

[0107] Figure 7 A general hardware environment 700 is shown in which the present disclosure may be applied, according to an exemplary embodiment of the present disclosure.

[0108] refer to Figure 7 , a computing device 700 will now be described as an example of a hardware device applicable to various aspects of the present disclosure. The computing device 700 can be any machine configured to perform processing and / or computing, and can be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a smartphone, a portable camera, or any combination thereof. The apparatus 100 described above can be implemented in whole or in part by the computing device 700 or a similar device or system.

[0109] The computing device 700 may include an element that can be connected to or communicate with the bus 702 via one or more interfaces. For example, the computing device 700 may include a bus 702, one or more processors 704, one or more input devices 706, and one or more output devices 708. The one or more processors 704 may be any type of processor and may include, but are not limited to, one or more general-purpose processors and / or one or more special-purpose processors (such as dedicated processing chips). The input device 706 may be any type of device that can input information to the computing device and may include, but are not limited to, a mouse, keyboard, touch screen, microphone, and / or remote control. The output device 708 may be any type of device that can present information and may include, but are not limited to, a display, a speaker, a video / audio output terminal, and / or a printer. The computing device 700 may also include or be connected to a non-volatile storage device 710, which may be any storage device that is non-volatile and can implement a data repository, and may include but is not limited to a disk drive, an optical storage device, a solid-state storage device, a floppy disk, a flexible disk, a hard disk, a magnetic tape or any other magnetic medium, a compact disk or any other optical medium, a ROM (read-only memory), a RAM (random access memory), a cache memory and / or any other memory chip or cartridge, and / or any other medium from which a computer can read data, instructions and / or code. The non-volatile storage device 710 may be removable from an interface. The non-volatile storage device 710 may have data / instructions / code for implementing the above-described methods and steps. The computing device 700 may also include a communication device 712. The communication device 712 may be any type of device or system capable of communicating with an external device and / or with a network, and may include but is not limited to a modem, a network card, an infrared communication device, wireless communication equipment and / or a communication medium such as Bluetooth. TM Chipsets for devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication facilities, etc.

[0110] The bus 702 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0111] The computing device 700 may also include a working memory 714 , which may be any type of working memory that can store instructions and / or data useful for the operation of the processor 704 , and may include, but is not limited to, random access memory and / or read-only memory devices.

[0112] Software elements may be located in the working memory 714, including, but not limited to, an operating system 716, one or more application programs 718, drivers, and / or other data and code. Instructions for executing the above-described methods and steps may be included in one or more application programs 718, and the components of the apparatus 100 may be implemented by the processor 704 reading and executing instructions from one or more application programs 718. More specifically, the scene design component 110 may be implemented, for example, by the processor 704 executing the application program 718 with instructions to execute step S210. The scene recognition component 120 may be implemented, for example, by the processor 704 executing the application program 718 with instructions to execute step S220 (or steps S2202, S2204, and S2206). The personnel determination component 130 may be implemented, for example, by the processor 704 executing the application program 718 with instructions to execute step S230 (or steps S2302, S2304, S2306, and S2308). The notification and display component 140 can be implemented, for example, by the processor 704 when executing an application 718 having instructions to perform step S240. The executable code or source code of the instructions of the software element can be stored in a non-transitory computer-readable storage medium (such as the aforementioned (one or more) storage devices 710) and can be read into the working memory 714 where it may be compiled and / or installed. The executable code or source code of the instructions of the software element can also be downloaded from a remote location.

[0113] From the above embodiments, it will be clear to those skilled in the art that the present disclosure can be implemented by software and necessary hardware, or can be implemented by hardware, firmware, etc. Based on this understanding, the embodiments of the present disclosure can be partially implemented in the form of software. Computer software can be stored in a computer-readable storage medium, such as a floppy disk, hard disk, optical disk, or flash memory. Computer software includes a series of instructions that enables a computer (such as a personal computer, a service station, or a network terminal) to run the method according to each embodiment of the present disclosure or a part thereof.

[0114] The disclosure having thus been described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the disclosure, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.

Claims

1. A personnel dispatching method in an emergency scenario, applied to an industrial site including multiple devices and a data collector for collecting data from each device, characterized in that: The method includes: receiving device identification data and device attribute data from a data collector, and identifying an emergency scenario based on the received data; and Determine personnel to dispatch based on the identified emergency scenario, The identifying of the emergency scenario based on the received data includes: identifying the emergency scenario according to a pre-established first undirected graph including a plurality of first nodes, a plurality of second nodes, and a plurality of edges, wherein the first nodes represent the preset emergency scenarios, the second nodes represent the device identification data and the device attribute data, and the edges connect the second nodes with the associated first nodes, Wherein, in the first undirected graph, each edge is provided with an associated threshold and weight, and wherein identifying an emergency scenario based on the received data further comprises: identifying an edge among the plurality of edges, for which the device attribute data represented by the corresponding second node and the corresponding threshold satisfy a preset condition, as a target edge; determining a score of the first node based on a corresponding weight of a target edge connected to the first node; and sorting the plurality of first nodes according to the scores of the first nodes, and determining one or more possible emergency scenarios as the identified emergency scenarios based on the sorting results, And wherein, the score of the first node is used to reflect the possibility of occurrence of the emergency scenario represented by it.

2. The method according to claim 1, wherein Identifying the emergency scenario based on the received data further includes associating the device identification data and the device attribute data from the data collector with the second node via a hash table.

3. The method according to claim 1, wherein The device identification data uniquely identifies the device, and the device attribute data includes electrical attribute data of the device and / or HVAC attribute data of the device.

4. The method according to claim 1, wherein The personnel to be dispatched based on the identified emergency scenario include: Personnel to be scheduled are determined based on a pre-established second undirected graph comprising a plurality of third nodes, a plurality of fourth nodes and a plurality of edges, wherein the third nodes represent personnel available for scheduling, the fourth nodes represent personal skills, and the edges connect the fourth nodes with associated third nodes, and wherein, in the second undirected graph, each edge is provided with an associated weight, which represents the proficiency of the personnel connected by the edge in the personal skills connected by the edge.

5. The method according to claim 4, wherein Determining personnel to dispatch based on the identified emergency scenario further includes: Determine the individual skills and proficiency required to handle the emergency scenario based on the identified emergency scenario; For each target fourth node representing the required personal skill, determining the difference between the proficiency level represented by the corresponding weight and the required proficiency level and the absolute value of the difference; For a third node connected to one or more target fourth nodes, determining a sum of difference values ​​corresponding to the connected target fourth nodes and a sum of absolute values ​​of the difference values; and The plurality of third nodes are sorted based on the sum of the differences and the sum of the absolute values ​​of the differences, and personnel to be dispatched are determined based on a result of the sorting.

6. The method according to claim 5, wherein: Sorting the plurality of third nodes further includes: sorting the plurality of third nodes in descending order of the sum of the difference values, and when the sum of the difference values ​​is equal, the smaller the sum of the absolute values ​​of the difference values, the higher the sorting.

7. The method according to claim 4, wherein: Determining personnel to dispatch based on the identified emergency scenario also includes determining personnel to dispatch based on the personnel's distance from the emergency scenario site.

8. The method according to claim 4, wherein: Determining personnel to dispatch based on the identified emergency scenario also includes determining personnel to dispatch based on the personnel's tool readiness.

9. The method according to claim 1, further comprising: The determined personnel to be dispatched are notified to arrive at the emergency scene site, and the operation guide for handling the emergency scene is notified to the determined personnel to be dispatched.

10. The method according to claim 1, further comprising: The location of the emergency scene and the location of the personnel determined to be dispatched are highlighted on the map, and the location movement of the personnel determined to be dispatched is also highlighted on the map.

11. The method according to claim 1, wherein The industrial site includes a data center.

12. The method according to claim 5, further comprising: Pre-establishing a first undirected graph; Pre-establish a second undirected graph; A correlation is established in advance between emergency scenarios and the personal skills and proficiency required to handle the emergency scenarios.

13. The method according to claim 12, further comprising: in, The first undirected graph and the relationship between emergency scenarios and the personal skills and proficiency required to handle the emergency scenarios are pre-established based on expert experience and / or pre-established by using machine learning technology, and the second undirected graph is pre-established based on a personnel information database.

14. An electronic device, characterized in that: include: Memory; and A processor coupled to the memory, wherein the memory stores instructions, and when the instructions are executed by the processor, the electronic device performs the method according to any one of claims 1-13.

15. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 13 is implemented.

16. A personnel dispatching device for emergency situations, characterized in that: include: Means for performing the method according to any one of claims 1-13.

17. A monitoring system for an industrial site, comprising: The electronic device according to claim 14.

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