Intelligent terminal rapid deployment and cooperative command method

Abstract

The invention provides an intelligent terminal rapid deployment and cooperative command method, which comprises the following steps: step 1, configuring a feature detection module, and collecting actual measurement feature data by the feature detection module according to preset sampling time; judging whether an emergency event occurs or not according to the actual measurement characteristic data and the actual measurement characteristic data change rate, dividing and marking the emergency event as a regional event and a co-processing event, and reporting and processing the co-processing event; 2, training a feature data prediction model, dividing and marking the regional events as controllable regional events and to-be-coordinated regional events according to data detected by the feature detection module and data predicted by the feature data prediction model, and reporting the to-be-coordinated regional events; 3, configuring an emergency plan database; 4, configuring a document template; and 5, configuring a teaching case library. The intelligent terminal rapid deployment and cooperative command method is used for emergency command, an emergency mechanism can be rapidly started, and an emergency processing scheme can be perfected.

Description

Technical Field

[0001] The present invention relates to the field of emergency command technology, and in particular to a method and device for rapid deployment and coordinated command of intelligent terminals. Background Art

[0002] The emergency command system refers to the activities of the government and other public institutions in the process of emergency prevention, incident response, in-event handling and post-event management, by establishing necessary response mechanisms and taking a series of necessary measures to protect the safety of public life and property and promote the harmonious and healthy development of society. In the event of an emergency, it is necessary to quickly activate the emergency mechanism, that is, through the emergency command system, various departments coordinate departments at all levels to standardize the emergency activation process, establish a startup mechanism, and promptly mobilize relevant emergency command personnel at all levels, and transmit accident site signals to leaders at all levels in real time at any time.

[0003] The patent (publication number CN116229672B) discloses a dangerous area emergency warning and rescue system based on big data, which quickly determines the warning method through the edge computing server and warns the monitoring target according to the warning method to avoid danger and reduce the accident rate. However, this method cannot reasonably classify the level of emergencies, which will reduce the efficiency of emergency command to a certain extent.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a method for rapid deployment and coordinated command of intelligent terminals, which is used for emergency command, can quickly start the emergency mechanism, and can improve the emergency handling plan.

[0006] In order to solve the above technical problems, the present invention provides a method for rapid deployment and coordinated command of intelligent terminals, comprising the following steps:

[0007] Step 1: Configure the feature detection module, which collects measured feature data according to the preset sampling time; determines whether an emergency event occurs based on the measured feature data and the rate of change of the measured feature data, and divides and marks the emergency event into regional events and collaborative processing events, and reports the collaborative processing events for processing;

[0008] Step 2: Train the feature data prediction model, divide and mark the regional events into controllable regional events and to-be-coordinated regional events according to the data detected by the feature detection module and the data predicted by the feature data prediction model, and report the to-be-coordinated regional events for processing;

[0009] Step 3: Configure the emergency plan database. When the emergency event is a collaborative processing event, match the emergency plan from the emergency plan database according to the data detected by the feature detection module, and configure the smart terminal and emergency command according to the matched emergency plan;

[0010] Step 4: Configure the document template. When the emergency event is a collaborative event, extract keywords from the location information of the emergency event, the data detected by the feature detection module, and the emergency plan matched from the emergency plan database and input them into the document template to generate a press release to be released. The press release to be released is used to release it to the public.

[0011] Step 5: Configure the teaching case library. The cases stored in the teaching case library include emergency response teaching cases for emergency personnel and public emergency response teaching cases. The emergency response teaching cases for emergency personnel are released to emergency personnel at preset intervals, and the public emergency response teaching cases are released to the public.

[0012] Further, in step 1, the feature detection module includes one or more of a local automatic detection module, a local manual statistics module and an Internet acquisition module. When there are multiple feature detection modules, the data detected by the multiple feature detection modules are normalized, and the normalized data are weighted to obtain feature data; a first threshold and a second threshold are preset, and when the measured feature data is greater than or equal to the first threshold and the rate of change of the measured feature data is greater than or equal to the second threshold, it is judged that an emergency event has occurred; a third threshold and a fourth threshold are preset, and when the measured feature data is greater than or equal to the third threshold and the rate of change of the measured feature data is greater than or equal to the fourth threshold, the emergency event is divided and marked as a collaborative processing event, otherwise the emergency event is defined as a regional event.

[0013] Furthermore, in step 1, when an emergency event occurs, the sampling time is updated and calculated according to the data detected by the feature detection module each time sampling is completed, and sampling is performed according to the updated sampling time. The calculation formula of the sampling time is as follows:

[0014]

[0015] In the formula, T represents the sampling time, k, a, and b are constants, D is the measured characteristic data, and D' is the rate of change of the measured characteristic data.

[0016] Further, in step 2, the feature data prediction model is used to calculate the predicted feature data of the current sampling time based on the measured feature data detected by the feature detection module at the previous sampling time, and calculate the predicted feature data change rate of the current sampling time based on the feature data detected by the measured feature detection module at the previous sampling time and the predicted feature data of the current sampling time, and calculate the feature data difference and the feature data change rate difference of the current sampling time; a fifth threshold and a sixth threshold are preset, and when the feature data difference is greater than or equal to the fifth threshold and the feature data change rate difference is greater than or equal to the sixth threshold, the regional event is divided and marked as an event to be coordinated and processed, otherwise the regional event is defined as a controllable regional event.

[0017] Further, in step 3, the emergency plan is matched through the following steps: input feature data, feature data change rate and text input, identify the first retrieval feature in the feature data, feature data change rate and text input through named entity recognition and relationship extraction technology, construct a first query graph, construct a second query graph according to the second retrieval feature connected to the first query graph, construct a retrieval subgraph according to the first query graph and the second query graph, and perform graph search according to the GpSI algorithm to obtain the emergency plan in the emergency plan database.

[0018] Further, in step 3, the intelligent terminal includes a power supply terminal and a communication terminal.

[0019] Further, in step 3, a microphone is configured in the communication terminal to obtain a running audio signal, and the audio signal is subjected to noise reduction processing through the following steps, the speech signal sampling frame is processed using a window function to obtain a first center number a[n], the first center number a[n] is subjected to Fourier transform to obtain a frequency domain signal A[k], and its amplitude spectrum B[k] is obtained, and the amplitude spectrum B'[k] is updated according to the following formula:

[0020] B'[k]=C[k]*B[k]e jd[k]

[0021] In the formula, d[k] is the phase spectrum, the preset threshold F[k], when B[k] is greater than or equal to F[k], C[k] is 1, when B[k] is less than F[k], C[k] is B[k] / F[k], and the amplitude spectrum B'[k] is updated.

[0022] Perform inverse Fourier transform to obtain the final speech signal.

[0023] Further, in step 4, the document template includes a document template for the general public and a document template for the public in the emergency area, and the document template includes a text template, and a keyword blank area is set in the text template.

[0024] The present invention provides a method for rapid deployment and coordinated command of intelligent terminals, which has the following beneficial effects: whether an emergency event occurs, and the classification of emergency events are quickly identified through measured feature data detected by a feature detection module and the rate of change of the measured feature data. Regional events are emergency events that can be handled independently in a certain area, and coordinated events are emergency events that have exceeded the processing capacity of a certain area and require coordinated processing by multiple regions and multiple departments, thereby realizing rapid activation of the emergency mechanism; for regional events, regional events can be further divided into controllable regional events and to-be-coordinated regional events according to the difference between predicted data and measured data. Controllable regional events are current emergency events that are controllable, and to-be-coordinated regional events are current emergency events that are controllable, but the development of the situation exceeds expectations, and there is a risk of being uncontrollable and requiring multi-regional coordination; by configuring emergency plan data to match emergency plans, rapid deployment and emergency command of intelligent terminals can be realized; by configuring document templates, when an emergency event occurs, a press release can be quickly generated for release to the public; by configuring a teaching case library, daily teaching can be carried out, thereby improving the emergency handling capabilities of the public and emergency personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flowchart of a method for rapid deployment and coordinated command of intelligent terminals. DETAILED DESCRIPTION

[0026] The core of the present invention is to provide a method for rapid deployment and coordinated command of intelligent terminals and emergency command.

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention is clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Embodiment 1:

[0029] A method for rapid deployment and coordinated command of intelligent terminals, such as Figure 1 As shown, the following steps are included:

[0030] Step 1: Configure the feature detection module, which collects measured feature data according to the preset sampling time; determines whether an emergency event occurs based on the measured feature data and the rate of change of the measured feature data, and divides and marks the emergency event into regional events and collaborative processing events, and reports the collaborative processing events for processing;

[0031] Step 2: Train the feature data prediction model, divide and mark the regional events into controllable regional events and to-be-coordinated regional events according to the data detected by the feature detection module and the data predicted by the feature data prediction model, and report the to-be-coordinated regional events for processing;

[0032] Step 3: Configure the emergency plan database. When the emergency event is a collaborative processing event, match the emergency plan from the emergency plan database according to the data detected by the feature detection module, and configure the smart terminal and emergency command according to the matched emergency plan;

[0033] Step 4: Configure the document template. When the emergency event is a collaborative event, extract keywords from the location information of the emergency event, the data detected by the feature detection module, and the emergency plan matched from the emergency plan database and input them into the document template to generate a press release to be released. The press release to be released is used to release it to the public.

[0034] Step 5: Configure the teaching case library. The cases stored in the teaching case library include emergency response teaching cases for emergency personnel and public emergency response teaching cases. The emergency response teaching cases for emergency personnel are released to emergency personnel at preset intervals, and the public emergency response teaching cases are released to the public.

[0035] The method for rapid deployment and coordinated command of intelligent terminals in this embodiment uses the measured feature data detected by the feature detection module and the rate of change of the measured feature data to quickly identify whether an emergency event has occurred, as well as the classification of emergency events. Regional events are emergency events that can be handled independently in a certain area, and coordinated events are emergency events that have exceeded the processing capacity of a certain area and require coordinated processing by multiple regions and multiple departments, thereby realizing the rapid activation of the emergency mechanism; for regional events, regional events can be further divided into controllable regional events and pending coordinated regional events based on the difference between predicted data and measured data. Controllable regional events mean that the current emergency event is controllable, and pending coordinated regional events mean that although the current emergency event is controllable, the development of the situation exceeds expectations, and there is a risk of uncontrollable and requiring multi-regional coordination; by configuring emergency plan data to match the emergency plan, rapid deployment and emergency command of intelligent terminals can be realized; by configuring document templates, when an emergency event occurs, a press release can be quickly generated for release to the public; by configuring a teaching case library, daily teaching can be carried out, thereby improving the emergency handling capabilities of the public and emergency personnel. In summary, the method for rapid deployment and coordinated command of intelligent terminals in this embodiment provides a complete emergency handling solution

[0036] In an optional embodiment, in step 1, the feature detection module includes one or more of a local automatic detection module, a local manual statistics module and an Internet acquisition module. When there are multiple feature detection modules, the data detected by the multiple feature detection modules are normalized, and the normalized data are weighted to obtain feature data; a first threshold and a second threshold are preset, and when the measured feature data is greater than or equal to the first threshold and the rate of change of the measured feature data is greater than or equal to the second threshold, it is judged that an emergency event has occurred; a third threshold and a fourth threshold are preset, and when the measured feature data is greater than or equal to the third threshold and the rate of change of the measured feature data is greater than or equal to the fourth threshold, the emergency event is divided and marked as a collaborative processing event, otherwise the emergency event is defined as a regional event.

[0037] In this optional embodiment, the local automatic detection module can be a smoke detection sensor, a camera, a toxic gas detector, etc., the local manual statistics module is a device that requires manual input from staff, and the Internet acquisition module refers to data obtained from the Internet, such as rainfall, etc. This optional embodiment sets up multiple feature detection modules to improve the accuracy of emergency event judgment.

[0038] In an optional embodiment, in step 1, when an emergency event occurs, the sampling time is updated and calculated according to the data detected by the feature detection module each time sampling is completed, and sampling is performed according to the updated sampling time. The calculation formula of the sampling time is as follows:

[0039]

[0040] In the formula, T represents the sampling time, k, a, and b are constants, D is the measured characteristic data, and D' is the rate of change of the measured characteristic data.

[0041] In this optional embodiment, the sampling time is dynamically adjusted. When the data detected by the feature detection module continues to increase, that is, when the emergency time continues to increase, the sampling time is shortened to improve the timeliness of emergency processing.

[0042] In an optional embodiment, in step 2, the feature data prediction model is used to calculate the predicted feature data of the current sampling time based on the measured feature data detected by the feature detection module at the previous sampling time, and calculate the predicted feature data change rate of the current sampling time based on the feature data detected by the measured feature detection module at the previous sampling time and the predicted feature data of the current sampling time, and calculate the feature data difference and the feature data change rate difference of the current sampling time; a fifth threshold and a sixth threshold are preset, and when the feature data difference is greater than or equal to the fifth threshold and the feature data change rate difference is greater than or equal to the sixth threshold, the regional event is divided and marked as an event to be coordinated and processed, otherwise the regional event is defined as a controllable regional event.

[0043] In this optional embodiment, the feature data prediction model may be a pre-trained neural network model. In this optional embodiment, the predicted feature data of the current sampling time is calculated based only on the measured feature data detected by the feature detection module of the previous sampling time, which has a higher accuracy.

[0044] In this optional embodiment, a fifth threshold and a sixth threshold are preset. When the difference in characteristic data is greater than or equal to the fifth threshold and the difference in the rate of change of characteristic data is greater than or equal to the sixth threshold, the regional event is divided and marked as an event to be coordinated and processed; otherwise, the regional event is defined as a controllable regional event; that is, the actual situation is greater than the predicted situation, and the difference exceeds a certain range. This indicates that although the current emergency event is controllable, the development of the situation has exceeded expectations, and there is a risk of being uncontrollable and requiring multi-regional coordination, which is then reported to enable the superior department to prepare in advance.

[0045] In an optional embodiment, in step 3, the emergency plan is matched through the following steps: feature data, feature data change rate and text input are input, a first retrieval feature in the feature data, feature data change rate and text input is identified through named entity recognition and relationship extraction technology, a first query graph is constructed, a second query graph is constructed according to a second retrieval feature connected to the first query graph, a retrieval subgraph is constructed according to the first query graph and the second query graph, and a graph search is performed according to the GpSI algorithm to obtain the emergency plan in the emergency plan database.

[0046] In an optional embodiment, in step 3, the intelligent terminal includes a power supply terminal and a communication terminal.

[0047] In this embodiment, the power supply terminal ensures the supply of power in the emergency command, and the communication terminal ensures the normal communication in the emergency command. The communication terminal may include relay equipment, audio and video acquisition and playback equipment, etc.

[0048] In an optional embodiment, in step 3, a microphone is configured in the communication terminal to obtain a running audio signal, and the audio signal is subjected to noise reduction processing through the following steps, a speech signal sampling frame is processed using a window function to obtain a first center number a[n], the first center number a[n] is subjected to Fourier transform to obtain a frequency domain signal A[k], and its amplitude spectrum B[k] is obtained, and the amplitude spectrum B'[k] is updated according to the following formula:

[0049] B'[k]=C[k]*B[k]e jd[k]

[0050] In the formula, d[k] is the phase spectrum, the preset threshold F[k], when B[k] is greater than or equal to F[k], C[k] is 1, when B[k] is less than F[k], C[k] is B[k] / F[k], and the amplitude spectrum B'[k] is updated.

[0051] Perform inverse Fourier transform to obtain the final speech signal.

[0052] In this optional embodiment, the audio signal collected by the microphone is subjected to noise reduction processing to improve the efficiency of emergency command.

[0053] In an optional embodiment, in step 4, the document template includes a document template for the general public and a document template for the public in the emergency area, and the document template includes a text template, and a keyword blank area is set in the text template.

[0054] In this optional embodiment, people in the emergency area refer to people within the corresponding scope of the emergency event, and general people refer to people outside the corresponding scope of the emergency event. Setting document templates for the general public and document templates for people in the emergency area can provide accuracy of the press release.

[0055] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention, and therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for rapid deployment and coordinated command of intelligent terminals, characterized in that: The steps include: Step 1: Configure the feature detection module, which collects measured feature data according to the preset sampling time; determines whether an emergency event occurs based on the measured feature data and the rate of change of the measured feature data, and divides and marks the emergency event into regional events and collaborative processing events, and reports the collaborative processing events for processing; Step 2: Train the feature data prediction model, divide and mark the regional events into controllable regional events and to-be-coordinated regional events according to the data detected by the feature detection module and the data predicted by the feature data prediction model, and report the to-be-coordinated regional events for processing; Step 3: Configure the emergency plan database. When the emergency event is a collaborative processing event, match the emergency plan from the emergency plan database according to the data detected by the feature detection module, and configure the smart terminal and emergency command according to the matched emergency plan; Step 4: Configure the document template. When the emergency event is a collaborative event, extract keywords from the location information of the emergency event, the data detected by the feature detection module, and the emergency plan matched from the emergency plan database and input them into the document template to generate a press release to be released. The press release to be released is used to release it to the public. Step 5: Configure the teaching case library. The cases stored in the teaching case library include emergency response teaching cases for emergency personnel and public emergency response teaching cases. The emergency response teaching cases for emergency personnel are released to emergency personnel at preset intervals, and the public emergency response teaching cases are released to the public.

2. According to claim 1, a method for rapid deployment and coordinated command of intelligent terminals is characterized in that: In step 1, the feature detection module includes one or more of a local automatic detection module, a local manual statistics module and an Internet acquisition module. When there are multiple feature detection modules, the data detected by the multiple feature detection modules are normalized, and the normalized data are weighted to obtain feature data; a first threshold and a second threshold are preset, and when the measured feature data is greater than or equal to the first threshold and the measured feature data change rate is greater than or equal to the second threshold, it is judged that an emergency event has occurred; a third threshold and a fourth threshold are preset, and when the measured feature data is greater than or equal to the third threshold and the measured feature data change rate is greater than or equal to the fourth threshold, the emergency event is divided and marked as a collaborative processing event, otherwise the emergency event is defined as a regional event.

3. The method for rapid deployment and coordinated command of intelligent terminals according to claim 1, characterized in that: In step 1, when an emergency event occurs, the sampling time is updated and calculated according to the data detected by the feature detection module each time sampling is completed, and sampling is performed according to the updated sampling time. The calculation formula of the sampling time is as follows: In the formula, T represents the sampling time, k, a, and b are constants, D is the measured characteristic data, and D' is the rate of change of the measured characteristic data.

4. The method for rapid deployment and coordinated command of intelligent terminals according to claim 1, characterized in that: In step 2, the feature data prediction model is used to calculate the predicted feature data of the current sampling time based on the measured feature data detected by the feature detection module at the previous sampling time, and calculate the predicted feature data change rate of the current sampling time based on the feature data detected by the measured feature detection module at the previous sampling time and the predicted feature data of the current sampling time, and calculate the feature data difference and feature data change rate difference of the current sampling time; A fifth threshold and a sixth threshold are preset. When the feature data difference is greater than or equal to the fifth threshold and the feature data change rate difference is greater than or equal to the sixth threshold, the regional event is divided and marked as an event to be coordinated and processed; otherwise, the regional event is defined as a controllable regional event.

5. According to the method for rapid deployment and coordinated command of intelligent terminals as claimed in claim 1, it is characterized in that: In step 3, the emergency plan is matched through the following steps: input feature data, feature data change rate and text input, identify the first retrieval feature in the feature data, feature data change rate and text input through named entity recognition and relationship extraction technology, construct a first query graph, construct a second query graph according to the second retrieval feature connected to the first query graph, construct a retrieval subgraph according to the first query graph and the second query graph, and perform graph search according to the GpSI algorithm to obtain the emergency plan in the emergency plan database.

6. A method for rapid deployment and coordinated command of intelligent terminals according to claim 1, characterized in that: In step 3, the intelligent terminal includes a power supply terminal and a communication terminal.

7. A method for rapid deployment and coordinated command of intelligent terminals according to claim 6, characterized in that: In step 3, a microphone is configured in the communication terminal to obtain the running audio signal, and the audio signal is subjected to noise reduction processing through the following steps. The voice signal sampling frame is processed using a window function to obtain the first center number a[n], the first center number a[n] is subjected to Fourier transform to obtain the frequency domain signal A[k], and its amplitude spectrum B[k] is obtained, and the amplitude spectrum B'[k] is updated according to the following formula: B’[k]=V[k]*B[k]e jd[k] In the formula, d[k] is the phase spectrum, the preset threshold F[k], when B[k] is greater than or equal to F[k], C[k] is 1, when B[k] is less than F[k], C[k] is B[k] / F[k], and the amplitude spectrum B'[k] is updated. Perform inverse Fourier transform to obtain the final speech signal.

8. A method for rapid deployment and coordinated command of intelligent terminals according to claim 1, characterized in that: In step 4, the document templates include document templates for the general public and document templates for people in the emergency area, and the document templates include text templates, and a keyword blank area is set in the text template.

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