Intelligent emergency scheduling system and method based on multi-modal data fusion
Through the intelligent emergency scheduling system with multimodal data fusion, the problem of inaccurate resource evaluation is solved, the scientificity and flexibility of resource scheduling are realized, and the efficiency and effectiveness of emergency scheduling are improved.
Patent Information
- Application Number
- CN202510474478.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-15
- Publication Date
- 2025-07-29
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing intelligent emergency dispatching system is difficult to accurately evaluate the resource status of different regions based on comprehensive data, and cannot accurately determine whether resources are over or insufficient, which affects the reasonable allocation of resources.
Through the intelligent emergency scheduling system of multimodal data fusion, including multimodal data acquisition module, regional resource evaluation module, resource scheduling processing module, scheduling analysis processing module and scheduling information output module, it comprehensively collects multimodal data, performs data cleaning and preprocessing, uses historical data for in-depth analysis, and generates flexible and scientific resource scheduling information.
Improve the efficiency and effectiveness of emergency scheduling, avoid waste of resources and improper allocation, and ensure the scientificity and flexibility of resource scheduling.
Smart Images
Figure CN120387635A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent dispatching technology, and in particular to an intelligent emergency dispatching system and method based on multimodal data fusion. Background Art
[0002] In the field of emergency dispatch, traditional methods rely on a single or limited data source, making it difficult to fully and accurately grasp the entire picture of emergency events. With the increasing frequency of disasters and accidents, emergency scenarios are becoming increasingly complex, placing higher demands on the timeliness, accuracy, and efficiency of emergency dispatch. The continuous development of technologies such as sensors, monitoring equipment, and information systems has made it possible to obtain multimodal data. Multimodal data fusion technology is also gradually being applied in various fields. Against this backdrop, intelligent emergency dispatch systems based on multimodal data fusion have emerged.
[0003] However, when using the existing intelligent emergency dispatch system, it is difficult to accurately assess the resource status of different regions based on comprehensive data, and it is impossible to accurately judge whether resources are excessive or insufficient, which affects the rational allocation of resources. Summary of the Invention
[0004] In response to the shortcomings of the existing technology, the present invention provides an intelligent emergency dispatch system and method based on multimodal data fusion, which solves the problem of being unable to accurately judge whether resources are excessive or insufficient, affecting the rational allocation of resources.
[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent emergency dispatch system based on multimodal data fusion, comprising:
[0006] The regional resource assessment module is used to analyze the multimodal data transmitted by the multimodal data acquisition module, classify different emergency areas according to the remaining resources, obtain regional classification information, including resource surplus areas and areas to be adjusted, and transmit it to the resource scheduling processing module;
[0007] The resource scheduling processing module performs scheduling analysis on resource-overflow areas and determines the maximum resource consumption and the average resource consumption based on the corresponding historical data. If the maximum resource consumption is regular, resource scheduling information is generated based on it. Otherwise, resource scheduling information is generated based on the resource consumption and transmitted to the scheduling information output module.
[0008] Perform scheduling analysis on the area to be adjusted, classify it according to whether there is secondary scheduling to obtain the secondary scheduling area, and transmit it to the scheduling analysis processing module;
[0009] The scheduling analysis and processing module is used to analyze the secondary scheduling area, calculate the secondary increase amount, scheduling difference and average total resource amount according to the corresponding historical data, sum them up to obtain the resource scheduling amount, and at the same time judge its scheduling satisfaction according to the historical data to generate a dissatisfaction analysis signal;
[0010] Process the dissatisfaction analysis signal, obtain the number of dissatisfaction times. If it is multiple times, generate resource scheduling information based on the maximum resource scheduling amount in the historical data and transmit it to the scheduling information output module.
[0011] As a further solution of the present invention, it further includes a multimodal data acquisition module and a scheduling information output module;
[0012] The multimodal data acquisition module is used to collect multimodal data through various sensors, monitoring devices, and information systems, and perform data cleaning, noise reduction, and normalization operations. At the same time, transmit the processed multimodal data to the regional resource evaluation module;
[0013] The scheduling information output module is used to display the obtained resource scheduling information to the corresponding management personnel.
[0014] As a further solution of the present invention, the specific method for the regional resource evaluation module to classify different emergency areas according to the remaining resource amount to obtain regional classification information is as follows:
[0015] All emergency areas are numbered as i in sequence, and i = 1, 2,..., j, where j is the total number of emergency areas. Obtain the total emergency resource amount and resource consumption amount of each emergency area i, calculate the remaining resource amount, and compare the remaining resource amount with the preset value. If the remaining resource amount is greater than the preset value, this emergency area is marked as an area with excessive resources;
[0016] If it is less than the preset value, it is marked as an area to be adjusted. After generating the regional classification information, it is transmitted to the resource scheduling processing module.
[0017] As a further solution of the present invention, the specific method for the resource scheduling processing module to perform scheduling analysis on the area with excessive resources is as follows:
[0018] All areas with excessive resources are numbered as a, and a = 1, 2,..., n, where n is the number of areas with excessive resources. Obtain the historical data of each area a, obtain the consumption times, corresponding resource consumption amount and maximum resource consumption amount, and calculate the average resource consumption;
[0019] Analyze the regularity of the maximum resource consumption amount. If there is a regular same situation, obtain the corresponding time point, generate resource scheduling information based on the time period where this time point is located and transmit it to the scheduling information output module; if not, generate scheduling information based on the average resource consumption.
[0020] As a further solution of the present invention, the specific way for the resource scheduling and processing module to perform scheduling analysis on the area to be adjusted is as follows:
[0021] Obtain all areas to be adjusted and classify the areas to be adjusted according to whether there is secondary scheduling to obtain secondary scheduling areas and areas to be adjusted. For the classified areas to be adjusted, no processing is performed.
[0022] As a further solution of the present invention, the specific way for the scheduling analysis and processing module to analyze the secondary scheduling areas is as follows:
[0023] Label all secondary scheduling areas as b, and b = 1, 2,..., m, where m represents the number of areas to be adjusted. Obtain the historical data of each area b to get the secondary scheduling times and secondary scheduling amounts, calculate the remaining scheduling amount, calculate the average value of all remaining scheduling amounts as the secondary increase amount, calculate the average value of the secondary scheduling amounts, find the difference between the two as the scheduling difference, obtain the average value of the initial scheduling resource amount, and add it to the scheduling difference to obtain the resource scheduling amount;
[0024] Obtain the resource scheduling amount corresponding to the secondary scheduling area, and determine whether there is a situation where the current resource scheduling amount is not satisfied in the historical data. If so, generate a dissatisfaction analysis signal. If not, no processing is performed and a resource scheduling information is generated.
[0025] As a further solution of the present invention, the specific way for the scheduling analysis module to process the dissatisfaction analysis signal is as follows:
[0026] Obtain the number of dissatisfaction times in the historical data. If the number of dissatisfaction times is 1, generate a scheduling information according to the existing resource scheduling amount. If it is multiple times, generate a comprehensive scheduling signal. For the comprehensive scheduling signal, generate a scheduling information from the historical data based on the maximum resource scheduling amount and transmit it to the scheduling information output module.
[0027] An intelligent emergency scheduling method based on multi-modal data fusion, which specifically includes the following steps:
[0028] Step S1: Collect multi-modal data through various sensors, monitoring devices, and information systems, and perform preprocessing operations on the obtained multi-modal data to obtain preprocessed data;
[0029] Step S2: Obtain the remaining resources in different regions according to the preprocessed data, compare them with the preset values, and generate region classification information through classification, including resource surplus regions and regions to be adjusted;
[0030] Step S3: Perform scheduling analysis on the resource surplus regions, calculate the average value of resource consumption according to the historical data, judge the maximum resource consumption amount, and generate resource scheduling information in combination with the average value of resource consumption;
[0031] Step S4: Conduct scheduling analysis on the area to be adjusted, classify the secondary scheduling area according to whether there is secondary scheduling, calculate the secondary increase corresponding to the secondary scheduling in combination with the corresponding historical data, and generate a resource scheduling volume in combination with the average value of the corresponding total resource volume;
[0032] Step S5: Judge the satisfaction of the resource scheduling volume according to the corresponding historical data, generate a dissatisfaction analysis signal, and at the same time perform resource scheduling according to the corresponding number of dissatisfaction times to generate resource scheduling information.
[0033] The present invention provides an intelligent emergency scheduling system and method based on multi-modal data fusion. Compared with the prior art, it has the following beneficial effects:
[0034] Through the multi-modal data acquisition module of the present invention, various types of data related to emergency events can be comprehensively collected, and preprocessing operations such as data cleaning, noise reduction, and normalization are performed to provide high-quality data for subsequent accurate analysis, solving the problem of one-sided data acquisition. The resource scheduling processing module and the scheduling analysis processing module conduct in-depth analysis using historical data for different area types. For example, analyze the law of the maximum resource consumption in the area with excessive resources, comprehensively calculate the resource scheduling volume for the secondary scheduling area, and adjust the strategy according to the historical satisfaction situation to generate flexible and scientific resource scheduling information, improving the efficiency and effect of emergency scheduling, and avoiding resource waste and improper allocation. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] Figure 1 It is a block diagram of the system principle of the present invention;
[0036] Figure 2 It is a method diagram of the steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0038] Embodiment 1
[0039] Please refer to Figure 1 , this application provides an intelligent emergency scheduling system based on multi-modal data fusion, including: a multi-modal data acquisition module, a regional resource evaluation module, a resource scheduling processing module, a scheduling analysis processing module, and a scheduling information output module, and in combination with Figure 1 It can be known that the above functional modules are connected in a one-way electrical connection.
[0040] Multimodal data acquisition module, which is used to collect multimodal data related to emergency events through various sensors, monitoring devices, information systems, etc., including but not limited to video images of the accident scene, sensor monitoring data (such as temperature, humidity, gas concentration, etc.), geographical location information, traffic data, meteorological data, and reports from relevant personnel. At the same time, preprocessing operations are performed on the obtained multimodal data. The preprocessing operations include data cleaning, noise reduction, and normalization operations, and the processed multimodal data is transmitted to the regional resource assessment module.
[0041] Regional resource assessment module, which is used to evaluate the resource status of different regions based on the obtained multimodal data, and the specific evaluation method is as follows:
[0042] First, comprehensively obtain all emergency areas and number them in sequence, denoted as i, where i = 1, 2,..., j, and here j represents the total number of emergency areas. For each emergency area i, obtain its total emergency resources, resource consumption, and remaining resources in detail. The remaining resources are obtained by subtracting the resource consumption from the total emergency resources, that is: remaining resources = total emergency resources - resource consumption. The operator needs to set a preset value for measuring the resource status according to the actual needs of the emergency situation. Compare and analyze the remaining resources of emergency area i with this preset value.
[0043] If the remaining resources are greater than the preset value, it indicates that the resources currently allocated to emergency area i exceed the actual requirements, and there is an excess situation. At this time, mark this emergency area as an area with excess resources. For example, an earthquake-stricken area is divided into 3 emergency areas (i.e., j = 3). Among them, the total emergency resources of emergency area 1 are 1000 copies of relief supplies. After a period of rescue, the resource consumption is 300 copies, and the remaining resources are 700 copies. The preset value set by the operator is 500 copies. Since 700 copies are greater than 500 copies, emergency area 1 is marked as an area with excess resources.
[0044] If the remaining resources are less than the preset value, it means that the resource reserve in emergency area i is not enough to meet the current rescue needs, that is, there is no excess situation in the allocated resources corresponding to this area, and it should be marked as an area to be adjusted. For example, the total emergency resources of emergency area 2 are 800 copies, the resource consumption is 600 copies, the remaining resources are 200 copies, and the preset value is 300 copies. Since 200 copies are less than 300 copies, emergency area 2 is marked as an area to be adjusted, and different areas are classified according to the above analysis, further generating area classification information, and at the same time transmitting the generated information to the resource scheduling and processing module.
[0045] Resource scheduling and processing module, which is used to perform different resource scheduling and processing according to the obtained area classification information, and the specific scheduling and processing method is as follows:
[0046] First, carry out scheduling analysis on the marked resource surplus areas in the regional classification information. Comprehensively obtain all resource surplus areas, number them in sequence, denoted as a, where a = 1, 2, …, n, and here n represents the total number of resource surplus areas. For each resource surplus area a, deeply mine its corresponding historical data. In the historical data, focus on extracting the resource consumption times information of this area. For each resource consumption event, record the corresponding resource consumption amount in detail, and at the same time find out the maximum resource consumption amount from it. In addition, through statistical calculation of all resource consumption amounts, obtain the resource consumption average value of this area, and the calculation method is the sum of all resource consumption amounts divided by the number of consumption times.
[0047] Next, conduct a regularity analysis on the obtained maximum resource consumption amount. The focus of this regularity analysis is to check whether there are regularly identical situations for the maximum resource consumption amount. For example, taking the time series as the dimension, observe whether the same value of the maximum resource consumption amount appears every fixed period (such as every week, every month, every quarter, etc.) in the past time period.
[0048] If it is found through analysis that there is such a regularly identical situation, then accurately obtain the time points corresponding to the maximum resource consumption amount. Using the time periods where these time points are located as the reference standard, generate the corresponding resource scheduling information. For example, if it is found that in a certain resource surplus area, the maximum resource consumption amount appears on the 15th day of each month in the past year, then the time period from the 14th to the 16th day of each month can be set as the key period for resource scheduling, and resource scheduling is arranged within this time period to allocate the surplus resources to other areas in need. The generated resource scheduling information includes details such as the scheduling time (starting from the 14th day of each month), the types and quantities of the scheduled resources (determined according to the surplus resource situation in this area), and the destination (other areas to be adjusted), and then transmit this information to the scheduling information output module.
[0049] If the analysis result shows that there is no regularly identical situation, then use the previously calculated resource consumption average value as the standard to generate resource scheduling information. For example, the resource consumption average value of a certain resource surplus area is 50 units of resources per consumption. According to this average value and the total amount of surplus resources in this area currently, reasonably plan the resource scheduling plan to determine the quantity of resources scheduled each time (such as setting it as 100 units of resources each time according to a certain multiple of the average value), the scheduling frequency (such as scheduling once every three days), etc. Similarly, transmit the resource scheduling information containing these details to the scheduling information output module for subsequent execution of the resource scheduling task.
[0050] Conduct scheduling analysis on the regions to be adjusted in the regional classification information, obtain all regions to be adjusted, and classify the regions to be adjusted according to whether there is secondary scheduling, obtaining the secondary scheduling regions and the regions to be adjusted. For the classified regions to be adjusted, no processing is performed, and the secondary scheduling region information is transmitted to the scheduling analysis and processing module;
[0051] The scheduling analysis and processing module is used to analyze the obtained secondary scheduling region information. First, conduct scheduling analysis on the regions to be adjusted in the regional classification information. Comprehensively obtain all regions to be adjusted and, based on the key factor of whether there is secondary scheduling, carefully divide the regions to be adjusted into two categories: secondary scheduling regions and ordinary regions to be adjusted. For the part determined to be ordinary regions to be adjusted after classification, no processing is performed at this stage and will be considered later according to the overall scheduling strategy.
[0052] Next, focus on the secondary scheduling regions. Obtain all secondary scheduling regions and label them sequentially, denoted as b, where b = 1, 2,..., m, and m represents the total number of secondary scheduling regions. For each secondary scheduling region b, deeply explore its corresponding historical data. From the historical data, accurately extract the number of secondary scheduling times of this region and the scheduling volume information corresponding to each secondary scheduling.
[0053] On this basis, calculate the remaining scheduling volume of each secondary scheduling region b. The remaining scheduling volume here refers to the total amount of resources remaining in this secondary scheduling region after the initial scheduling and previous secondary schedulings. Statistically average the remaining scheduling volumes of all secondary scheduling regions, and the calculated average value is denoted as the secondary increase. For example, assume there are 3 secondary scheduling regions, and their remaining scheduling volumes are 80 units, 100 units, and 120 units respectively. Then the secondary increase = (80 + 100 + 120) ÷ 3 = 100 units.
[0054] Similarly, statistically average the secondary scheduling volumes of all secondary scheduling regions and calculate the average value of the secondary scheduling volume. For example, the secondary scheduling volumes of these 3 secondary scheduling regions are 30 units, 40 units, and 50 units respectively. Then the average value of the secondary scheduling volume = (30 + 40 + 50) ÷ 3 = 40 units.
[0055] Then, calculate the difference between the average value of the secondary scheduling volume and the secondary increase. This difference is what we call the scheduling difference. In the above example, the scheduling difference = 40 - 100 = -60 units.
[0056] Further, obtain the average value of the total resource amount corresponding to the secondary scheduling area. The average value of the total resource amount here refers to the average of the resource amounts allocated to these secondary scheduling areas during the initial scheduling. Suppose the resource amounts of these 3 secondary scheduling areas during the initial scheduling are 200 units, 220 units, and 240 units respectively. Then the average value of the total resource amount = (200 + 220 + 240) ÷ 3 = 220 units.
[0057] Determine the resource scheduling amount by calculating the sum of the numerical values of the average value of the total resource amount and the scheduling difference. In this example, the resource scheduling amount = 220 + (-60) = 160 units.
[0058] For each secondary scheduling area, after obtaining its corresponding resource scheduling amount, deeply analyze the historical data to determine whether there is a situation where the currently calculated resource scheduling amount cannot meet the past actual requirements. For example, in the past secondary scheduling history, there was a period when the actual required resource amount was as high as 200 units, while the currently calculated resource scheduling amount was only 160 units. This situation belongs to the case where the resource scheduling amount is insufficient. If such a situation exists, generate a non - satisfaction analysis signal for subsequent in - depth analysis of the reasons and adjustment of the scheduling strategy. If, after carefully checking the historical data, there is no such situation where the resource scheduling amount is insufficient, then this area does not require additional processing and directly generates detailed resource scheduling information.
[0059] When a non - satisfaction analysis signal is generated, for the secondary scheduling area corresponding to this signal, more in - depth processing work needs to be carried out. First, accurately extract the non - satisfaction times information related to this secondary scheduling area from the historical data. The non - satisfaction times refer to the statistical count of the number of times when the currently calculated resource scheduling amount cannot meet the actual requirements in the past resource scheduling practice.
[0060] If it is statistically found that the non - satisfaction times are only once, this indicates that this situation may be an individual phenomenon caused by accidental factors. In this case, no special treatment is required for the time being, and directly use the previously calculated resource scheduling amount as the standard to generate detailed resource scheduling information. The resource scheduling information should comprehensively cover key elements such as the types of resources scheduled, the exact quantity, the planned scheduling time, and the clear destination. For example, a secondary scheduling area A generates a non - satisfaction analysis signal. After checking the historical data, its non - satisfaction times are 1 time. The previously calculated resource scheduling amount for this area is to allocate 500 medical supply units to this area, the scheduling time is 10 am the next day, and the destination is the central rescue point within the area. Based on the judgment that the non - satisfaction times are one, directly generate resource scheduling information according to this resource scheduling amount to guide subsequent resource allocation work.
[0061] If the number of times does not meet the requirement of being multiple, this means that there are relatively significant problems in the resource scheduling of this secondary scheduling area, rather than an accidental situation. At this time, a comprehensive scheduling signal needs to be generated to comprehensively review and adjust the resource scheduling strategy.
[0062] For the generated comprehensive scheduling signal, deeply obtain the historical data corresponding to this secondary scheduling area again. Among these historical data, focus on finding the maximum resource scheduling volume in all past resource scheduling plans. Using this maximum resource scheduling volume as a new standard, generate resource scheduling information. For example, when the comprehensive scheduling signal is generated for secondary scheduling area B, through in-depth mining of its historical data, it is found that in multiple past resource scheduling, the maximum resource scheduling volume occurred during a major disaster rescue, when 800 tents were scheduled to this area. Based on this maximum resource scheduling volume, generate new resource scheduling information, such as scheduling 750 tents (reasonably adjusted near the maximum considering the actual situation) to the designated concentrated resettlement point in area B this time, and the scheduling time is arranged two days later, ensuring that on the basis of fully considering historical needs, it can meet the possible peak resource demand currently, optimize the resource scheduling strategy, and improve the rescue efficiency.
[0063] The scheduling information output module is used to display the obtained resource scheduling information to the corresponding management personnel.
[0064] Embodiment 2
[0065] Please refer to Figure 2 , this application provides an intelligent emergency scheduling method based on multi-modal data fusion, and the method specifically includes the following steps:
[0066] Step S1: Collect multi-modal data through various sensors, monitoring devices, and information systems, and perform preprocessing operations on the obtained multi-modal data to obtain preprocessed data;
[0067] Step S2: Obtain the remaining resources in different regions according to the preprocessed data, compare with the preset value and classify to generate region classification information, including resource surplus regions and regions to be adjusted, and the specific processing method is the same as the processing process of the region resource evaluation module in Embodiment 1;
[0068] Step S3: Conduct scheduling analysis on the resource surplus regions, calculate the average resource consumption according to historical data, judge the maximum resource consumption, and generate resource scheduling information in combination with the average resource consumption, and the specific processing method is the same as the processing process of the resource scheduling processing module in Embodiment 1;
[0069] Step S4: Conduct scheduling analysis on the area to be adjusted. Classify the secondary scheduling area according to whether there is secondary scheduling, calculate the secondary increase corresponding to the secondary scheduling in combination with the corresponding historical data, and generate a resource scheduling volume in combination with the average value of the corresponding total resource volume. The specific processing method is the same as the processing process of the scheduling analysis processing module in Embodiment 1;
[0070] Step S5: Judge the satisfaction of the resource scheduling volume according to the corresponding historical data, generate a dissatisfaction analysis signal, and at the same time conduct resource scheduling according to the corresponding number of dissatisfaction times to generate resource scheduling information. The specific processing method is the same as the processing process of the scheduling analysis processing module in Embodiment 1;
[0071] For some data in the above formula, only their numerical values are taken for calculation, and the parameter units are not substituted for calculation. At the same time, the content not described in detail in this specification belongs to the prior art well known to those skilled in the art.
[0072] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.
Claims
1. An intelligent emergency dispatch system based on multi-modal data fusion, characterized in that, Including: A regional resource assessment module, which is used to analyze the multi-modal data transmitted by the multi-modal data collection module, classify different emergency regions according to the remaining resources to obtain regional classification information, including resource surplus regions and regions to be adjusted, and transmit it to the resource scheduling and processing module; A resource scheduling and processing module, which conducts scheduling analysis on resource surplus regions, determines the maximum resource consumption and the average resource consumption based on the corresponding historical data. If the maximum resource consumption shows a pattern, it generates resource scheduling information based on this standard; otherwise, it generates resource scheduling information based on the resource consumption, and transmits it to the scheduling information output module; Conducts scheduling analysis on regions to be adjusted, classifies them into secondary scheduling regions according to whether there is secondary scheduling, and transmits them to the scheduling analysis and processing module; A scheduling analysis and processing module, which is used to analyze the secondary scheduling regions, calculate the secondary increase, scheduling difference, and the average total resource volume based on the corresponding historical data, sum them up to obtain the resource scheduling volume, and at the same time judge its scheduling satisfaction according to the historical data to generate a dissatisfaction analysis signal; Processes the dissatisfaction analysis signal, obtains the number of dissatisfaction times. If it is multiple times, it generates resource scheduling information based on the maximum resource scheduling volume in the historical data and transmits it to the scheduling information output module.
2. The intelligent emergency dispatching system based on multimodal data fusion according to claim 1, characterized in that It also includes a multi-modal data collection module and a scheduling information output module; The multi-modal data collection module is used to collect multi-modal data through various sensors, monitoring devices, and information systems, perform data cleaning, noise reduction, and normalization operations, and at the same time transmit the processed multi-modal data to the regional resource assessment module; The scheduling information output module is used to display the obtained resource scheduling information to the corresponding management personnel.
3. The intelligent emergency dispatch system based on multi-modal data fusion according to claim 1, characterized in that The specific method for the regional resource assessment module to classify different emergency regions according to the remaining resources to obtain regional classification information is as follows: All emergency regions are numbered as i in sequence, and i = 1, 2,..., j, where j is the total number of emergency regions. Obtain the total emergency resources and resource consumption of each emergency region i, calculate the remaining resources, and compare the remaining resources with the preset value. If the remaining resources are greater than the preset value, this emergency region is marked as a resource surplus region; If it is less than the preset value, it is marked as a region to be adjusted. After generating the regional classification information, it is transmitted to the resource scheduling and processing module.
4. The intelligent emergency dispatch system based on multi-modal data fusion according to claim 1, characterized in that, The specific method for the resource scheduling and processing module to conduct scheduling analysis on resource surplus regions is as follows: All resource surplus regions are numbered as a, and a = 1, 2,..., n, where n is the number of resource surplus regions. Obtain the historical data of each region a, obtain the consumption times, the corresponding resource consumption, and the maximum resource consumption, and calculate the average resource consumption; Analyze the regularity of the maximum resource consumption. If there is a regular same situation, obtain the corresponding time point, and generate resource scheduling information based on the time period where this time point is located and transmit it to the scheduling information output module; if not, generate scheduling information based on the average resource consumption.
5. The intelligent emergency dispatch system based on multi-modal data fusion according to claim 1, characterized in that, The specific method for the resource scheduling and processing module to conduct scheduling analysis on regions to be adjusted is as follows: Obtain all regions to be adjusted and classify the regions to be adjusted according to whether there is secondary scheduling, obtaining the secondary scheduling regions and the regions to be adjusted. For the classified regions to be adjusted, no processing is performed.
6. The intelligent emergency dispatching system based on multi-modal data fusion according to claim 1, characterized in that, The specific method for the scheduling analysis and processing module to analyze the secondary scheduling regions is as follows: Label all secondary scheduling regions as b, where b = 1, 2,..., m, and m represents the number of regions to be adjusted. Obtain the historical data of each region b to get the secondary scheduling times and the secondary scheduling volume, calculate the remaining scheduling volume, calculate the average value of all remaining scheduling volumes as the secondary increase amount, calculate the average value of the secondary scheduling volume, find the difference between the two as the scheduling difference, obtain the average value of the initial scheduling resource volume, and add it to the scheduling difference to obtain the resource scheduling volume. Obtain the resource scheduling volume corresponding to the secondary scheduling region and determine whether there is a situation where the current resource scheduling volume is not satisfied in the historical data. If it exists, generate a dissatisfaction analysis signal. If it does not exist, do not process it and generate a resource scheduling information.
7. The intelligent emergency dispatch system based on multi-modal data fusion according to claim 1, characterized in that The specific method for the scheduling analysis module to process the dissatisfaction analysis signal is as follows: Obtain the number of dissatisfaction times in the historical data. If the number of dissatisfaction times is 1, generate a scheduling information according to the existing resource scheduling volume. If it is multiple times, generate a comprehensive scheduling signal. For the comprehensive scheduling signal, generate a scheduling information based on the maximum resource scheduling volume from the historical data and transmit it to the scheduling information output module.
8. An intelligent emergency dispatch method based on multimodal data fusion, which is executed by the intelligent emergency dispatch system according to any one of claims 1-7, characterized in that This method specifically includes the following steps: Step S1: Collect multi-modal data through various sensors, monitoring devices, and information systems, and perform preprocessing operations on the obtained multi-modal data to obtain preprocessed data. Step S2: Obtain the remaining resource volume of different regions according to the preprocessed data, compare it with the preset value, and classify and generate region classification information, including resource surplus regions and regions to be adjusted. Step S3: Conduct scheduling analysis on the resource surplus regions, calculate the average resource consumption according to the historical data, judge the maximum resource consumption, and generate resource scheduling information in combination with the average resource consumption. Step S4: Conduct scheduling analysis on the regions to be adjusted, classify the secondary scheduling regions according to whether there is secondary scheduling, calculate the secondary increase amount corresponding to the secondary scheduling in combination with the corresponding historical data, and generate the resource scheduling volume in combination with the corresponding total resource volume average value. Step S5: Judge the satisfaction of the resource scheduling volume according to the corresponding historical data, generate a dissatisfaction analysis signal, and at the same time perform resource scheduling according to the corresponding number of dissatisfaction times to generate resource scheduling information.