Full-process digital management method and system for volunteer service activities

Abstract

The invention relates to the technical field of volunteer service management, provides a full-process digital management method and system for volunteer service activities, and is used for solving the problems of poor accuracy and low execution efficiency of large volunteer service activity personnel allocation in the prior art. The method comprises the steps that historical activity data, real-time state data, activity area environment state information and activity progress data of volunteers are acquired, the historical data are subjected to structured processing, then the real-time data are combined, and a behavior portrait is constructed through a deep neural network; meanwhile, performing space-time analysis on the environment information to form environment evaluation data; fusing the behavior portrait and the environment evaluation data by adopting a multi-source fusion algorithm, and generating fusion decision data through dynamic matching degree calculation; decision data and progress data are jointly processed based on a reinforcement learning decision model, a task allocation scheme is generated, and dynamic digital management of the whole process of volunteer service is achieved. According to the invention, the accuracy and execution efficiency of large-scale volunteer service activity personnel allocation are improved.

Description

Technical Field

[0001] This application relates to the field of volunteer service management technology, and in particular to a digital management method and system for the entire process of volunteer service activities. Background Technology

[0002] As the scale of large-scale urban events continues to expand, volunteer service management faces significant challenges in coordinating multi-dimensional tasks. Such events require the dynamic deployment of hundreds of volunteers across different areas, while simultaneously responding in real time to multiple variables such as changes in personnel status, fluctuations in environmental factors, and adjustments to task progress. Traditional management methods that rely on fixed schedules exhibit lag in response to dynamic factors such as sudden personnel movements or environmental changes. On the other hand, location-based scheduling systems struggle to adequately account for individual volunteer capabilities and changes in their real-time status.

[0003] In existing technical solutions, there is a volunteer dispatch system based on real-time positioning. This system collects the location information of volunteers through special devices worn by volunteers and generates corresponding shortest path plans by combining the distribution of preset task points. In addition, the system uses an electronic map to intuitively display the real-time distribution status of volunteers and adopts a polling mechanism to continuously update the status information of each task. When the system detects that the number of volunteers in a certain service area is lower than a preset threshold, it will automatically send specific task instructions to nearby volunteers. This solution uses electronic fence technology to accurately delineate the boundaries of each service area, and then realizes the push and confirmation feedback of task instructions through the volunteers' mobile terminals.

[0004] However, these systems have gradually revealed some inherent limitations in practical applications. For example, their ability to perceive environmental parameters is mainly limited to basic geographical location information; in terms of volunteer ability assessment, the system mainly relies on historical participation records for static scoring; when faced with tasks that require multi-regional collaboration, the system adopts a sequential execution mechanism, which leads to significant response delays in cross-regional resource allocation; in addition, the system's task allocation strategy usually leans towards a simple proximity principle, and this single-dimensional decision-making mode can easily produce suboptimal scheduling schemes in complex real-world scenarios. Summary of the Invention

[0005] This application provides a digital management method and system for the entire process of volunteer service activities, in order to solve the problems of poor accuracy and low execution efficiency in the allocation of personnel for large-scale volunteer service activities in the prior art.

[0006] To address the aforementioned technical problems, firstly, this application provides a method for the full-process digital management of volunteer service activities, comprising:

[0007] Acquire historical activity data, real-time status data, environmental status information of the target activity area, and activity progress data during the execution of volunteer service activities;

[0008] The historical activity data is structured, and based on the structured historical activity data and the real-time status data, a behavioral profile is generated using a deep neural network.

[0009] The environmental status information is analyzed in both spatiotemporal dimensions to generate environmental assessment data;

[0010] A multi-source data fusion algorithm is used to fuse the behavioral profile with the environmental assessment data. During the fusion process, the degree of fit between the volunteer’s ability and the environmental needs is evaluated by dynamic matching degree calculation to generate fusion decision data for volunteer service.

[0011] By using a reinforcement learning decision model, the fused decision data and the activity progress data are jointly processed to generate a task allocation scheme. Based on the task allocation scheme, the volunteer service activities are dynamically managed digitally throughout the entire process.

[0012] Secondly, this application provides a full-process digital management system for volunteer service activities, including:

[0013] The acquisition module is used to acquire historical activity data, real-time status data, environmental status information of the target activity area, and activity progress data during the execution of volunteer service activities.

[0014] The generation module is used to perform structured processing on the historical activity data, and based on the structured historical activity data, combined with the real-time status data, to generate a behavioral profile through a deep neural network.

[0015] The analysis module is used to perform spatiotemporal analysis on the environmental state information to generate environmental assessment data;

[0016] The fusion module is used to use a multi-source data fusion algorithm to fuse the behavioral profile with the environmental assessment data. During the fusion process, the dynamic matching degree is used to calculate and assess the degree of fit between the volunteer's ability and the environmental needs, so as to generate fusion decision data for volunteer services.

[0017] The management module is used to jointly process the fused decision data and the activity progress data using a reinforcement learning decision model to generate a task allocation scheme, and to perform dynamic management of the entire volunteer service activity in a digital manner based on the task allocation scheme.

[0018] Thirdly, this application provides an electronic device, comprising:

[0019] Memory, used to store computer programs;

[0020] A processor is used to execute the computer program to implement the steps of the full-process digital management method for volunteer service activities as described in the first aspect above.

[0021] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the full-process digital management method for volunteer service activities as described in the first aspect above.

[0022] The technical solution provided in this application has the following beneficial effects:

[0023] First, digital collection of all elements of volunteer service was achieved by acquiring various data sources. Based on this, historical activity data was structured and integrated with real-time status data. Then, a deep neural network was used to generate behavioral profiles that accurately depict the comprehensive service capabilities of volunteers. Simultaneously, in-depth spatiotemporal analysis of environmental status information was conducted to generate quantitative environmental assessment data. Subsequently, a multi-source data fusion algorithm was used to effectively integrate the generated behavioral profiles with the environmental assessment data, and dynamic matching degree calculations were used to generate more comprehensive fusion decision data. Finally, a reinforcement learning decision model was used to jointly process the fusion decision data and activity progress data to generate an optimized task allocation scheme, achieving adaptive dynamic management of the entire volunteer service process.

[0024] Furthermore, multi-dimensional data such as temperature, humidity, population density, and sound intensity are extracted from environmental status information, and intuitive temperature distribution heatmaps and humidity change trend maps are generated respectively. At the same time, dynamic clustering analysis effectively identifies different population gathering areas and their flow paths, and sound source localization technology is combined to determine the precise location of noise sources. Finally, through spatiotemporal overlay analysis and a preset evaluation model, the environmental comfort index and population density index of each grid unit are calculated, thereby generating comprehensive environmental assessment data covering multi-dimensional information. This processing can construct a multi-dimensional environmental situation assessment system, providing accurate environmental situation information support for subsequent task decisions.

[0025] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a full-process digital management method for volunteer service activities provided in this application embodiment;

[0028] Figure 2 A schematic diagram illustrating a specific implementation of a full-process digital management method for volunteer service activities provided in this application embodiment;

[0029] Figure 3 This is a schematic diagram of the structure of a full-process digital management system for volunteer service activities provided in an embodiment of this application. Detailed Implementation

[0030] To address the problems existing in current technologies, this application proposes a full-process digital management method for volunteer service activities. This method aims to collaboratively handle the comprehensive capabilities of volunteers, the real-time status of the environment, and the overall progress of the activity. Specifically, it first integrates the historical performance and real-time status of volunteers to form their behavioral profiles, while simultaneously performing spatiotemporal analysis of environmental data to generate a dynamic environmental status assessment. Next, it utilizes multi-source data fusion technology to calculate the dynamic matching degree between volunteers' individual capabilities and environmental task requirements. Finally, it relies on a reinforcement learning model to generate an adaptively adjustable task allocation scheme. This technical approach breaks through the limitations of single-dimensional decision-making in traditional scheduling. The key lies in establishing a dynamic correlation model between environmental elements and task requirements, thereby achieving real-time perception and response to volunteer status. It not only effectively alleviates the delay problem in resource allocation in multi-regional collaborative scenarios but also improves the accuracy and dynamic adaptability of the volunteer service management process.

[0031] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] The core of this application is to provide a digital management method for the entire process of volunteer service activities, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0033] Step 101: Obtain historical activity data, real-time status data, environmental status information of the target activity area, and activity progress data during the execution of volunteer service activities.

[0034] In step 101, historical activity data refers to the skills, expertise, and service efficiency information recorded by volunteers when they participated in services in the past, while real-time status data includes the volunteer's current location trajectory and physical exertion.

[0035] Environmental status information consists of physical parameters such as temperature, humidity, and crowd density collected by sensors deployed in the activity area. Activity progress data reflects the comparison between task completion status and time progress.

[0036] For example, in an international marathon, volunteer A's historical data shows that he has completed medical first aid skills certification 3 times, with an average response time of 2.5 minutes. Real-time data indicates that the volunteer is currently at the 5km mark of the course, with a physical exertion index of 0.6. Meanwhile, environmental data shows that the temperature at the starting area is 28 degrees Celsius, the humidity is 65%, and the population density is 1.8 people per square meter. In addition, progress data indicates that the race is currently 35% complete. All of these data together form the basis of this analysis.

[0037] Step 102: Perform structured processing on the historical activity data, and generate a behavioral profile based on the structured historical activity data and the real-time status data using a deep neural network.

[0038] In step 102, the behavioral profile is a comprehensive representation of a volunteer's abilities formed by fusing multi-dimensional features through a deep neural network.

[0039] In this embodiment, historical activity data is first structured and parsed, skill records are classified and coded according to a preset system, and service timeliness is statistically analyzed according to different time segments. Then, trajectory analysis and status assessment are performed on real-time status data, and the assessment results and historical features are input into a deep neural network. Subsequently, the deep neural network transforms and combines these features through its multi-layer hidden units, and finally outputs a comprehensive behavioral profile that combines ability assessment and status assessment.

[0040] For example, in the data processing of volunteer A, multiple sets of feature data were extracted. The first set of feature data included a medical emergency score of 0.9 and a foreign language translation score of 0.8. The second set of feature data included a response score of 0.85 and an endurance score of 0.9. The third set of feature data was an efficiency score of 0.8, and the fourth set of feature data involved a physical strength index of 0.7 and a physical exertion rate of 0.1. After all these feature values ​​were input into the neural network, the data underwent three layers of nonlinear transformation processing to finally generate a corresponding behavioral profile. The profile had a comprehensive ability score of 0.82 and a real-time status score of 0.75.

[0041] Step 103: Perform spatiotemporal dimension analysis on the environmental state information to form environmental assessment data.

[0042] In step 103, the environmental assessment data is a quantitative evaluation of the environmental conditions within the region, which includes the environmental comfort index and the population density index.

[0043] In this embodiment, the environmental status information is first analyzed from multiple dimensions. A continuous distribution map is generated based on temperature and humidity data using spatial interpolation. Specific clustering areas and main movement paths are identified using clustering algorithms based on population data. The actual distribution of noise is determined using sound source localization technology based on sound data. Next, these analyzed elements are uniformly placed in a gridded space for overlay analysis, and the environmental index for each spatial unit is calculated using a specialized environmental assessment model. It should be noted that the specific implementation process of step 103 is described in steps 201-205 below, and will not be repeated here.

[0044] For example, during the event environment analysis, the track was divided into 50-meter grid units. The temperature distribution heat map showed that the temperature in the starting area was between 28 and 30 degrees Celsius, while the humidity change map reflected the trend of humidity gradually increasing from 65% to 70%. Crowd clustering identified a primary gathering area of ​​approximately 200 square meters near the starting arch, and flow path analysis clearly showed the main flow of people in this area. In addition, noise localization further determined the specific location of the cheering area in the stadium. Finally, the model calculated that the environmental comfort index of the starting area was 0.76, and the population density index of the area reached 0.88.

[0045] Step 104: Using a multi-source data fusion algorithm, the behavioral profile and the environmental assessment data are fused using multi-source features. During the fusion process, the degree of fit between the volunteer's ability and the environmental needs is assessed through dynamic matching degree calculation to generate fusion decision data for volunteer services.

[0046] In step 104, the dynamic matching degree is calculated through vector space, reflecting the degree of adaptation between volunteers and environmental needs. The "dynamic" aspect is reflected in the following aspects: by continuously acquiring real-time status data of volunteers and environmental status information, the matching degree calculation can be dynamically adjusted according to the real-time changes in volunteers' physical exertion, location changes, and environmental conditions. At the same time, combined with the continuous updating of activity progress data, it is ensured that the matching degree assessment can adapt to the constantly changing needs during the activity.

[0047] Data integration serves as the basis for decision-making regarding volunteer scheduling and service allocation.

[0048] In this embodiment, the behavioral profile and environmental situation data are first mapped to a vector space, and then the basic matching degree is calculated by the cosine of the angle. Next, the time decay factor is calculated by combining the progress data of the activity, and the basic matching degree is dynamically corrected by the factor. Then, the corresponding fitness sequence is generated according to the corrected matching degree, and finally the final decision data is generated from the sequence by the optimal combination screening.

[0049] For example, the ability vector of volunteer A [0.9, 0.8, 0.85, 0.9, 0.8, 0.7, 0.1] and the demand vector of the destination area [0.3, 0.9, 0.8] are calculated using the cosine of the angle, resulting in a basic matching degree of 0.85. When the activity is halfway through, a time decay factor of 0.9 is introduced, and the matching degree becomes 0.765 after correction. In the formed fitness sequence, the matching degree of volunteer A with the destination area is 0.765, and the matching degree with the supply station is 0.648. After optimal combination screening, the final decision is to assign volunteer A to the destination area.

[0050] Step 105: Using a reinforcement learning decision model, the fused decision data and the activity progress data are jointly processed to generate a task allocation scheme. Based on the task allocation scheme, the volunteer service activity is dynamically managed digitally throughout the entire process.

[0051] In step 105, the task allocation scheme is a volunteer scheduling plan generated based on multi-dimensional analysis; the dynamic management of the entire process is digitalized, which includes adjusting the task allocation in real time according to changes in the environment and the status of volunteers, and can continuously optimize the resource scheduling scheme according to the progress of the activity.

[0052] In this embodiment, the fused decision data and activity progress data are first encoded into a feature vector, which is then input into a reinforcement learning decision model. In the model, the state evaluation network calculates the value of the current state through a multi-layer neural network, while the policy network outputs a specific allocation scheme based on this value evaluation. The effect data collected after the instruction is executed is fed back to the model, thereby completing the parameter update of the model and realizing closed-loop optimization.

[0053] For example, firstly, the scheduling priority of volunteer A (0.9) and the information of the destination area assigned to volunteer A are jointly encoded as state features, and the task completion rate (0.65) and time progress (0.58) are encoded as progress features. After these features are input into the reinforcement learning model, the state evaluation network outputs a value evaluation result of 0.82, and the policy network generates a plan to send volunteer A to the destination area based on this result. After the plan is executed, the effect data collected, such as the arrival time of 3.5 minutes and the processing efficiency of 89%, will be fed back to the model to update its parameters.

[0054] This method effectively improves resource utilization efficiency and enhances the timeliness of dispatch response in the management of volunteer services for large-scale events, thereby improving the overall effectiveness of volunteer service management.

[0055] To address the accuracy issue in environmental situation assessment, in some embodiments, step 103 involves performing spatiotemporal analysis on the environmental state information to generate environmental assessment data, such as... Figure 2 As shown, it includes:

[0056] Step 201: Extract temperature data, humidity data, crowd density data, and sound intensity data from the environmental status information.

[0057] In step 201, the temperature data is the air thermodynamic parameter collected by the environmental sensor, the humidity data is the air moisture content parameter collected by the environmental sensor, the crowd density data is the number of people per unit area counted by the visual sensor, and the sound intensity data is the environmental noise level parameter collected by the acoustic sensor.

[0058] Step 202: Analyze the temperature data to generate a temperature distribution heatmap, and process the humidity data to generate a humidity change map.

[0059] In step 202, the temperature distribution heatmap is a temperature visualization distribution map generated by a spatial interpolation algorithm, and the humidity change map is a spatial path map reflecting the change of humidity over time.

[0060] In this embodiment, a continuous temperature distribution heatmap is generated using the Kriging interpolation algorithm for the temperature data, while a humidity change map is generated by time series analysis and combined with its spatial location information for the humidity data. These two maps jointly characterize the features of the environmental parameters from the two dimensions of spatial distribution and temporal change.

[0061] Step 203: Perform dynamic clustering analysis on the crowd density data to identify crowd gathering areas and movement paths, and perform sound source localization analysis on the sound intensity data to determine the location of the noise source.

[0062] In step 203, the crowd gathering area is the concentrated distribution area of ​​people identified by density clustering, the flow path is the personnel movement channel obtained by trajectory analysis, and the noise source location is the coordinate of the noise source determined by sound source localization technology.

[0063] In this embodiment of the application, a density-based clustering algorithm is used to identify the first and second regions based on the crowd density data. At the same time, a trajectory tracking algorithm is used to analyze the movement patterns of people and generate flow paths. The time difference of arrival method is used to determine the specific spatial distribution location of the noise source based on the sound intensity data.

[0064] Step 204: Perform spatiotemporal overlay analysis on the temperature distribution heat map, the humidity change map, the crowd gathering area, the flow path, and the noise source location, and combine it with the evaluation model to calculate the environmental comfort index and population density index of each grid unit.

[0065] In step 204, the environmental comfort index is a quantitative indicator that characterizes the human body's comfort perception of the environment, the population density index is a quantitative indicator that reflects the degree of population gathering in the area, and the grid cell is a basic analysis unit that divides the activity area into fixed sizes.

[0066] Step 205: Generate environmental assessment data based on the environmental comfort index and the population density index.

[0067] In this embodiment of the application, based on the environmental comfort index and population density index of each grid cell, regional environmental assessment data is generated through spatial aggregation and weight allocation. This data fully reflects the environmental conditions and population distribution characteristics of the activity area.

[0068] In this embodiment, a precise environmental situation awareness system was established through collaborative analysis and quantitative evaluation of multi-dimensional environmental parameters, and reliable environmental data support was provided for the dynamic management of volunteer services, thereby effectively improving the accuracy of environmental adaptation for large-scale events and the scientific nature of personnel scheduling.

[0069] To improve the precision of environmental situation assessment, in some embodiments, step 204 involves: performing spatiotemporal overlay analysis on the temperature distribution heatmap, the humidity change map, the crowd gathering area, the flow path, and the noise source location, and combining this with the assessment model to calculate the environmental comfort index and population density index for each grid cell, including:

[0070] Step 301: Divide the target activity area into multiple grid units according to the preset geographic grid.

[0071] In this embodiment of the application, the grid size is set according to the terrain features and service requirements of the activity area, and the entire area is divided into several uniform grid units, wherein each grid serves as an independent spatial analysis unit to carry out subsequent environmental parameter calculations.

[0072] Step 302: Overlay the temperature distribution heat map and the humidity change map, and based on the overlay result, combine the thermodynamic equations to calculate the environmental comfort value of each grid cell using a convolutional neural network.

[0073] In step 302, the environmental comfort value is a quantitative index of thermal comfort calculated by comprehensively considering temperature and humidity factors, reflecting the basic comfort feeling of the human body under specific temperature and humidity conditions; the thermodynamic equation is expressed as follows:

[0074] ;

[0075] in This represents the predicted average vote value. This represents the human metabolic rate, expressed in W / m². This represents the mechanical work done by the human body, measured in units of 1. , This represents the partial pressure of water vapor in the environment, expressed in kPa. This represents the air temperature, measured in °C. This equation is used to quantify the thermal comfort sensation of the human body in a specific environment.

[0076] In this embodiment, step 302 is implemented as follows: First, the temperature distribution heatmap and humidity change map are overlaid to form a comprehensive environmental layer, which contains temperature and humidity information; then, the comprehensive environmental layer is input into a convolutional neural network, which extracts the spatial features of temperature and humidity in local areas through convolutional layers and combines these features with thermodynamic equations for calculation; finally, the convolutional features and thermodynamic calculation results are fused through fully connected layers to output the environmental comfort value of each grid cell. For example, when the temperature of a certain grid cell is 28°C and the humidity is 65%, its environmental comfort value can be calculated to be 0.72.

[0077] Step 303: Perform density clustering analysis on the population gathering area to identify the first region and the second region.

[0078] In step 303, the first area is the core area where people are highly concentrated, and the second area is the surrounding area where people are relatively dispersed. Together, they constitute the spatial pattern of population distribution.

[0079] In this embodiment of the application, step 303 can use a density clustering algorithm to analyze the population distribution data. Specifically, firstly, the neighborhood radius ε = 5 meters and the minimum number of contained points MinPts = 10 are set as clustering parameters. Then, starting from any unvisited data point, all data points with reachable density are found to form a cluster. The area with a population density greater than or equal to 3 people / square meter is marked as the first area, and the area with a density between 1 and 3 people / square meter is marked as the second area. For example, in the marathon starting area, the area of ​​the first area centered on the arch is identified as 200 square meters, and the area of ​​the second area extending around it is 500 square meters.

[0080] Step 304: Based on the first region and the second region, and in conjunction with the flow path, calculate the personnel flow intensity of each grid cell.

[0081] In step 304, the population flow intensity is a quantitative indicator that reflects the level of activity of people entering and leaving the grid cell, and is used to reflect the population flow characteristics of the area.

[0082] Step 305: Perform spatial correlation analysis between the location of the noise source and the area where the crowd gathers, and calculate the noise impact coefficient.

[0083] In step 305, the noise impact coefficient is a quantitative parameter that characterizes the degree of impact of noise on areas where people gather. It can be used to reflect the spatial correlation between noise sources and population distribution.

[0084] In this embodiment of the application, the noise impact coefficient of each grid cell is obtained by calculating the relative distance and orientation between the noise source and the area where the crowd gathers, and combining the noise intensity data.

[0085] Step 306: Input the environmental comfort value, the personnel flow intensity and the noise impact coefficient into the evaluation model, and calculate the environmental comfort index of each grid cell through the feature intersection module in the evaluation model.

[0086] In this embodiment, the environmental comfort value, personnel flow intensity and noise impact coefficient are fused and weighted by the feature cross module in the model, and finally the environmental comfort index of each grid cell is output.

[0087] Step 307: Calculate the population density index of each grid cell by evaluating the population density analysis module in the model.

[0088] In this embodiment of the application, the population density analysis module in the evaluation model is used to calculate the population density index of each grid cell by combining the distribution characteristics of the population gathering area and the population flow intensity data.

[0089] In this embodiment, the quantitative assessment and spatial visualization of the environmental situation are achieved through grid-based fine analysis and multi-dimensional parameter fusion, and accurate environmental data support is provided for the dynamic scheduling of volunteer services, thereby improving the environmental adaptability and the accuracy of resource allocation for large-scale events.

[0090] To further improve the accuracy of population flow intensity calculation, in some embodiments, step 304: based on the first region and the second region, and in conjunction with the flow path, calculate the population flow intensity of each grid cell, including:

[0091] Step 401: Identify the intersection points of the flow paths within the grid cells, and collect personnel flow data for each intersection point.

[0092] In step 401, the path intersection point is the location where the flow path intersects within the grid cell, and the personnel flow data is the statistics of the number of people passing through the path intersection point per unit time.

[0093] In this embodiment, the location of the path intersection point within each grid cell is first identified, and then the number of people passing through each intersection point is counted using video surveillance equipment and mobile signal monitoring technology, thereby forming a population flow dataset.

[0094] Step 402: Analyze the flow direction distribution of the personnel flow data between the first area and the second area, and calculate the inflow and outflow of personnel in each grid cell based on the flow direction distribution.

[0095] In step 402, the flow direction distribution is the directional characteristic of personnel moving between different areas, the personnel inflow is the number of personnel entering the grid cell, and the personnel outflow is the number of personnel leaving the grid cell.

[0096] In this embodiment of the application, step 402 can be implemented through the following process: First, determine the positional relationship of each grid cell relative to the first area and the second area, and then use a direction sensor or trajectory tracking technology to count the movement direction of people passing through the boundary line of the grid cell within a specific time period; for each grid cell, the flow of people moving towards the center point of the first area is counted as the inflow, while the flow of people moving away from the center point of the first area and towards the second area is counted as the outflow.

[0097] For example, in a grid cell A located at the edge of the first region, analysis reveals that within a 1-minute time window, 50 people cross the grid boundary from the second region and enter the cell. This number is counted as the inflow. At the same time, 30 people cross the boundary from the cell and move to other second regions. This number is counted as the outflow. Therefore, the inflow of people in this grid cell is 50 people / minute, and the outflow of people is 30 people / minute.

[0098] Step 403: Calculate the population flow intensity of each grid cell based on the difference between the population inflow and the population outflow.

[0099] In this embodiment of the application, based on the inflow and outflow data of personnel in each grid cell, the real-time personnel flow intensity, which reflects the net change in personnel flow, is obtained by calculating the difference between the two.

[0100] In this embodiment of the application, the quantitative analysis of the dynamic distribution of people is realized by accurately calculating the personnel flow intensity of grid cells, which provides accurate personnel flow data support for environmental comfort assessment and volunteer scheduling.

[0101] To further improve the intelligence level of volunteer service management, in some embodiments, step 105 involves: using a reinforcement learning decision model to jointly process the fused decision data and the activity progress data to generate a task allocation scheme; and based on the task allocation scheme, performing dynamic, full-process digital management of volunteer service activities, including:

[0102] Step 501: Encode the scheduling priority and regional allocation scheme in the fused decision data into a state feature vector.

[0103] In step 501, the state feature vector is a feature representation formed by digitally encoding the scheduling priority and regional allocation scheme in the fused decision data.

[0104] In this embodiment, the scheduling priority values ​​and regional allocation scheme identifiers in the fused decision data are first standardized, and then the information is converted into a fixed-dimensional numerical vector through feature encoding technology, thereby forming a state feature vector representing the current scheduling state.

[0105] Step 502: Encode the task completion rate and time progress in the activity progress data into a progress feature vector.

[0106] In step 502, the progress feature vector is a feature representation formed by quantizing and encoding the task completion rate and time progress in the activity progress data.

[0107] In this embodiment of the application, the task completion ratio and time progress ratio in the activity progress data are normalized, and then transformed into a numerical vector with uniform dimensions through a feature mapping method to form a progress feature vector representing the activity progress.

[0108] Step 503: Input the state feature vector and the progress feature vector into the state evaluation network of the reinforcement learning decision model, and generate the state value evaluation result by performing feature transformation and nonlinear mapping through the multi-layer neural units in the state evaluation network.

[0109] In step 503, the state value assessment result is a quantitative score of the value of the current system state by the state assessment network.

[0110] In this embodiment, step 503 can be implemented through the following process: First, the state feature vector and the progress feature vector are concatenated into a comprehensive feature vector, and then this comprehensive vector is input into the state evaluation network. The network contains three fully connected layers, where the first layer uses the ReLU activation function to perform nonlinear mapping on the input features and extract 128-dimensional features, the second layer compresses the feature dimension to 64 dimensions again using the ReLU function, and the third layer outputs a scalar value after linear transformation. This scalar value is normalized by the Sigmoid function and can be used as the state value evaluation result to represent the quality of the current system state.

[0111] For example, when the input state feature vector is [0.8, 0.6, 0.9] representing scheduling priority, and the progress feature vector is [0.7, 0.5] representing task completion rate and time progress, the combined vector [0.8, 0.6, 0.9, 0.7, 0.5] is processed by a three-layer network, and the final output state value evaluation result is 0.82. This result indicates that the current system state is at a relatively good level.

[0112] Step 504: Based on the state value assessment results, generate a task allocation scheme for volunteers through the policy network of the reinforcement learning decision model.

[0113] In this embodiment of the application, the policy network of the reinforcement learning decision model receives the state value evaluation result as input, and after network calculation, outputs the optimal volunteer task allocation strategy, thereby forming a specific task allocation scheme.

[0114] Step 505: Generate task assignment instructions and region adjustment instructions according to the task allocation scheme.

[0115] In step 505, the task assignment instruction is a specific work task allocation command, and the area adjustment instruction is a work area change instruction.

[0116] In this embodiment of the application, the specific implementation process of step 505 may be as follows: First, parse the specific content in the task allocation scheme to extract the volunteer identifier, target service position and target work area information that need to be scheduled; then, bind the volunteer identifier with the target position number to generate a task assignment instruction according to the preset instruction format template, and bind the volunteer identifier with the target area coordinates to generate an area adjustment instruction; finally, encapsulate these instructions into a standard data format and send them to the volunteer terminal device.

[0117] For example, when the task allocation plan determines to move volunteer A from the current area to the medical aid station, a task assignment instruction bound to volunteer A and post MB001, as well as an area adjustment instruction bound to volunteer A and area Z005, will be generated. These instructions will be sent to volunteer A's mobile terminal in real time via the wireless network. After receiving the instructions, the terminal device will immediately update the interface display and issue a prompt sound.

[0118] Step 506: Execute the task assignment instruction and the area adjustment instruction to update the volunteer's service position and work area in real time, and collect task execution effect data during the real-time update process.

[0119] In step 506, the task execution effect data is feedback information that records the instruction execution process and results.

[0120] Step 507: Feed the execution effect data back to the reinforcement learning decision model to update the parameters of the policy network and realize closed-loop dynamic management of volunteer service activities.

[0121] It should be understood that the specific implementation process of steps 506 to 507 can be referred to relevant technologies, and will not be repeated here.

[0122] In this embodiment, adaptive decision-making for volunteer service scheduling is achieved through continuous optimization and closed-loop management of the reinforcement learning model, thereby improving the accuracy of task allocation and execution efficiency.

[0123] To further improve the accuracy of volunteer service decision-making, in some embodiments, step 104 involves using a multi-source data fusion algorithm to fuse the behavioral profile with the environmental assessment data. During the fusion process, a dynamic matching degree is used to calculate the degree of fit between the volunteer's abilities and environmental needs, thereby generating fused decision data for volunteer services. This includes:

[0124] Step 601: Based on the behavioral profile, generate a capability vector; based on the environmental assessment data, generate an environmental demand vector.

[0125] In step 601, the capability vector is a numerical representation of each capability feature in the behavioral profile, and the environmental demand vector is a numerical representation of each demand feature in the environmental assessment data.

[0126] In this embodiment, key capability features are extracted from behavioral profiles and standardized to form a fixed-dimensional capability vector. At the same time, core requirement features are extracted from environmental assessment data and quantified to form a corresponding environmental requirement vector.

[0127] Step 602: In the vector space, calculate the cosine value of the angle between the capability vector and the environmental demand vector, and use the cosine value of the angle as the basic matching degree.

[0128] In step 602, the basic matching degree is the degree of similarity between the capability vector and the environmental requirement vector in the vector space.

[0129] In this embodiment of the application, the ability vector and the environmental requirement vector are first mapped to the same vector space. Then, the similarity between the two vectors is evaluated by calculating the cosine value of the angle between the two vectors. Therefore, the cosine value can be used as a basic matching degree to reflect the initial fit level between the volunteer's ability and the environmental requirement.

[0130] Step 603: Dynamically correct the basic matching degree based on the time decay factor, which is calculated based on the activity progress data.

[0131] In step 603, the time decay factor is a dynamic adjustment coefficient calculated based on the activity execution progress, used to reflect the impact of the activity progress on the matching degree.

[0132] In this embodiment of the application, a time decay factor is calculated based on the activity progress data. This factor changes continuously as the activity progresses. Then, the basic matching degree is multiplied by the time decay factor to achieve dynamic correction of the matching degree.

[0133] Step 604: Generate a sequence of suitability between volunteers and volunteer service positions based on the dynamically corrected matching degree.

[0134] In step 604, the fit sequence is an ordered set that records the dynamically adjusted match between all volunteers and positions.

[0135] In this embodiment, step 604 can be implemented as follows: First, a matching degree record is established for each volunteer with all available volunteer service positions, and the dynamically corrected matching degree values ​​are stored according to the volunteer number and the position number; then, for each volunteer service position, all volunteers are sorted from high to low matching degree for that position to form a candidate sequence for each position; at the same time, for each volunteer, their matching degree for all positions is sorted from high to low to form a sequence of available positions for each volunteer; finally, a complete fit degree sequence containing all volunteer and position pairings and their corresponding matching degrees is generated.

[0136] For example, after dynamic adjustment, volunteer A has a match degree of 0.85 with the medical position, 0.72 with the guidance position, and 0.68 with the supplies position. Volunteer B has match degrees of 0.78, 0.91, and 0.65 with these three positions, respectively. The generated fitness sequence is {(A, medical, 0.85), (A, guidance, 0.72), (A, supplies, 0.68), (B, medical, 0.78), (B, guidance, 0.91), (B, supplies, 0.65)}. This sequence will serve as the basis for subsequent optimal fit combination selection.

[0137] Step 605: Sort and filter the fitness sequence, select the optimal fitness combination, and generate fusion decision data based on the optimal fitness combination.

[0138] In step 605, the optimal fit combination is the volunteer job allocation scheme with the best overall matching effect selected from the fit sequence; the fit sequence is a set of dynamically adjusted matching values ​​between each volunteer and each volunteer service position, and it is a list containing multiple matching values.

[0139] In this embodiment, the fitness sequence can be subjected to multi-objective optimization analysis based on a comprehensive consideration of the overall matching effect and distribution balance, so as to select the optimal combination of volunteer positions and then generate the final fusion decision data based on the combination.

[0140] In this embodiment, multi-source data fusion and dynamic matching degree calculation are used to achieve accurate matching between volunteer capabilities and environmental needs, and provide a scientific basis for volunteer service scheduling, thereby improving the rationality of human resource allocation and service efficiency.

[0141] To further improve the accuracy of behavioral profiling, in some embodiments, step 102 involves: structuring the historical activity data, and generating a behavioral profile based on the structured historical activity data and the real-time status data using a deep neural network, including:

[0142] Step 701: Extract skill expertise information and service timeliness information from the historical activity data, and extract location trajectory data and physical exertion data from the real-time status data.

[0143] In step 701, the skills and expertise information is a record of the professional abilities demonstrated by the volunteer in past services, the service timeliness information is a record of the volunteer's response speed and service duration in historical activities, the location trajectory data is a record of the volunteer's movement path within the activity area, and the physical exertion data is a record of the changes in the volunteer's physical load during the service process.

[0144] In this embodiment of the application, skill and expertise information and service timeliness information from historical activity data are extracted from the volunteer database, while location trajectory data and physical exertion data are obtained from real-time monitoring devices, providing a complete data foundation for subsequent feature extraction.

[0145] Step 702: Process the skill expertise information, service timeliness information, location trajectory data and physical exertion data accordingly to obtain first feature data, second feature data, third feature data and fourth feature data.

[0146] The first characteristic data is standardized data of skill expertise information after being classified and organized according to a unified standard; the second characteristic data is quantitative data of service timeliness information after being statistically processed according to the time dimension; the third characteristic data is mobility characteristic data obtained by path pattern analysis of location trajectory data; and the fourth characteristic data is status assessment data formed by trend analysis of physical exertion data.

[0147] Specifically, step 702 includes the following steps: Step a1: Classify and organize the skill expertise information according to a preset skill classification system to form first feature data.

[0148] In step a1, the preset skill classification system refers to a set of volunteer service skill classification standards that are predefined before the system is implemented. For example, skills are divided into specific categories such as medical first aid, language translation, and order maintenance. Each category contains corresponding skill levels and requirements, which are used to classify and organize volunteer skill expertise information in a unified and standardized manner.

[0149] In this embodiment of the application, skill expertise information is first categorized according to a preset skill classification system, and then each skill category is graded and standardized to form structured first feature data.

[0150] Step a2: The service timeliness information is segmented and statistically analyzed according to the time dimension to form the second feature data.

[0151] In this embodiment of the application, the service timeliness information is segmented and statistically analyzed according to different time periods, and then the average response time and continuous service duration are calculated. Finally, the second feature data is formed through normalization processing.

[0152] Step a3: Perform path analysis on the location trajectory data according to the preset movement trajectory to form third feature data.

[0153] In step a3, the movement trajectory refers to the movement route with a specific pattern identified by path analysis of the volunteer's location trajectory data. The movement trajectory includes typical volunteer service movement patterns such as straight-line movement, circular patrol, and fixed-point duty. These patterns are predefined according to the characteristics of volunteer service activities.

[0154] In this embodiment of the application, path analysis is performed on the location trajectory data to identify features such as movement speed, movement distance and activity range, and the data is classified and statistically analyzed according to a preset movement trajectory pattern to finally form third feature data.

[0155] Step a4: Assess the physical exertion data according to the trend of physical exertion changes to form the fourth feature data.

[0156] In step a4, the physical strength change trend refers to the pattern of physical strength change identified by time-series analysis of volunteers' physical strength consumption data. This pattern of physical strength change includes typical change patterns such as continuous physical strength consumption, rapid decline in physical strength, and stable physical strength. These patterns are pre-established based on human kinematic characteristics and volunteer service intensity characteristics.

[0157] In this embodiment of the application, trend analysis is performed on physical exertion data to determine indicators such as the rate of change of physical exertion and the current physical exertion level, and fourth feature data is generated through a state assessment model.

[0158] Step 703: Input the first feature data, the second feature data, the third feature data, and the fourth feature data into a deep neural network, and generate a behavioral profile through multi-level feature extraction and aggregation by the deep neural network.

[0159] In this embodiment, the first feature data, the second feature data, the third feature data, and the fourth feature data are input into a deep neural network. Then, the network extracts and combines these data through multiple hidden layers, and finally outputs a behavioral profile that includes comprehensive capability assessment and real-time status assessment.

[0160] In this embodiment of the application, a comprehensive and accurate behavioral profile is constructed through multi-dimensional data fusion and deep neural network analysis, providing reliable data support for subsequent task allocation and dynamic scheduling.

[0161] Figure 3 A schematic diagram of the structure of a full-process digital management system for volunteer service activities provided in this application embodiment is shown below. Figure 3 As shown, the system includes:

[0162] The acquisition module 31 is used to acquire historical activity data, real-time status data, environmental status information of the target activity area, and activity progress data during the execution of volunteer service activities.

[0163] The generation module 32 is used to perform structured processing on the historical activity data, and based on the structured historical activity data, combined with the real-time status data, to generate a behavioral profile through a deep neural network.

[0164] Analysis module 33 is used to perform spatiotemporal dimension analysis on the environmental state information to form environmental assessment data.

[0165] The fusion module 34 is used to use a multi-source data fusion algorithm to fuse the behavioral profile with the environmental assessment data. During the fusion process, the dynamic matching degree is used to calculate and assess the degree of fit between the volunteer's ability and the environmental needs, so as to generate fusion decision data for volunteer services.

[0166] The management module 35 is used to use a reinforcement learning decision model to jointly process the fused decision data and the activity progress data to generate a task allocation scheme, and to perform dynamic management of the entire process of volunteer service activities in a digital manner according to the task allocation scheme.

[0167] The full-process digital management system for volunteer service activities in this application is used to implement the aforementioned full-process digital management method for volunteer service activities. Therefore, the specific implementation of the full-process digital management system for volunteer service activities can be found in the embodiment section of the full-process digital management method for volunteer service activities above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0168] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the full-process digital management method for volunteer service activities described above.

[0169] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the full-process digital management method for volunteer service activities described above.

[0170] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0171] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the full-process digital management method for volunteer service activities.

[0172] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0173] The above provides a detailed description of the digital management method and system for the entire process of volunteer service activities provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for full-process digital management of a volunteer service activity, characterized in that, The method comprises the following steps: acquiring historical activity data of volunteers, real-time state data, environmental state information of a target activity area, and activity progress data during execution of a volunteer service activity; structurally processing the historical activity data, and generating a behavior portrait based on the structurally processed historical activity data in combination with the real-time state data through a deep neural network; performing spatio-temporal dimension analysis on the environmental state information to form environmental assessment data; adopting a multi-source data fusion algorithm to perform multi-source feature fusion on the behavior portrait and the environmental assessment data, and evaluating the adaptation degree of volunteer ability and environmental demand through dynamic matching degree calculation during the fusion process to generate fusion decision data for the volunteer service; processing the fusion decision data and the activity progress data jointly through a reinforcement learning decision model to generate a task allocation scheme, and dynamically managing the volunteer service activity in an entire process according to the task allocation scheme.

2. The method for full-process digital management of volunteer service activities according to claim 1, characterized in that, The spatio-temporal dimension analysis on the environmental state information to form environmental assessment data comprises the following steps: extracting temperature data, humidity data, crowd density data and sound intensity data from the environmental state information; analyzing the temperature data to generate a temperature distribution thermal map, and processing the humidity data to generate a humidity change graph; performing dynamic clustering analysis on the crowd density data to identify crowd gathering areas and flow paths, and performing sound source positioning analysis on the sound intensity data to determine noise source positions; performing spatio-temporal superposition analysis on the temperature distribution thermal map, the humidity change graph, the crowd gathering areas, the flow paths and the noise source positions, and calculating environmental comfort index and personnel density index of each grid unit in combination with an evaluation model; generating environmental assessment data based on the environmental comfort index and the personnel density index.

3. The method for full-process digital management of volunteer service activities according to claim 2, characterized in that, The spatio-temporal superposition analysis on the temperature distribution thermal map, the humidity change graph, the crowd gathering areas, the flow paths and the noise source positions, and the calculation of environmental comfort index and personnel density index of each grid unit in combination with an evaluation model comprise the following steps: dividing the target activity area into a plurality of grid units according to a preset geographical grid; superimposing the temperature distribution thermal map and the humidity change graph, and calculating the environmental comfort value of each grid unit through a convolutional neural network in combination with a thermodynamic equation based on the superposition result; performing density clustering analysis on the crowd gathering areas to identify a first area and a second area; calculating the personnel flow intensity of each grid unit based on the first area and the second area in combination with the flow paths; performing spatial correlation analysis on the noise source positions and the crowd gathering areas to calculate a noise influence coefficient; inputting the environmental comfort value, the personnel flow intensity and the noise influence coefficient into the evaluation model, and calculating the environmental comfort index of each grid unit through a feature cross module in the evaluation model; calculating the personnel density index of each grid unit through a crowd density analysis module in the evaluation model.

4. The method for full-process digital management of volunteer service activities according to claim 3, characterized in that, The personnel flow intensity of each grid cell is calculated based on the first area and the second area in combination with the flow path, including: A path intersection point of the flow path in the grid cell is identified, and personnel flow data passing through each path intersection point is counted; The flow direction distribution of the personnel flow data between the first area and the second area is analyzed, and the personnel inflow and the personnel outflow in each grid cell are calculated based on the flow direction distribution; The personnel flow intensity of each grid cell is calculated based on the difference between the personnel inflow and the personnel outflow.

5. The method for full-process digital management of volunteer service activities according to claim 1, characterized in that, The reinforcement learning decision model is used to jointly process the fusion decision data and the activity progress data to generate a task allocation scheme, and the task allocation scheme is used to dynamically manage the whole process of the volunteer service activity, including: The scheduling priority and the regional allocation scheme in the fusion decision data are encoded into a state feature vector; The task completion rate and the time progress in the activity progress data are encoded into a progress feature vector; The state feature vector and the progress feature vector are input into a state evaluation network of the reinforcement learning decision model, and feature transformation and nonlinear mapping are performed through multiple layers of neural units in the state evaluation network to generate a state value evaluation result; A task allocation scheme for volunteers is generated based on the state value evaluation result through a policy network of the reinforcement learning decision model; Task assignment instructions and regional adjustment instructions are generated based on the task allocation scheme; The task assignment instructions and the regional adjustment instructions are executed to update the service post and the work area of the volunteers in real time, and task execution effect data is collected during the real-time updating process; The execution effect data is fed back to the reinforcement learning decision model to complete the parameter update of the policy network and realize closed-loop dynamic management of the volunteer service activity.

6. The method for full-process digital management of volunteer service activities according to claim 1, characterized in that, The multi-source data fusion algorithm is used to perform multi-source feature fusion on the behavior portrait and the environment assessment data, and the adaptation degree of the volunteer's ability and the environmental demand is evaluated through dynamic matching degree calculation during the fusion process to generate fusion decision data for volunteer service, including: An ability vector is generated based on the behavior portrait, and an environmental demand vector is generated based on the environment assessment data; The cosine of the angle between the ability vector and the environmental demand vector is calculated in the vector space, and the cosine of the angle is taken as the basic matching degree; The basic matching degree is dynamically modified based on a time decay factor, and the time decay factor is calculated based on the activity progress data; An adaptation degree sequence of the volunteers and the volunteer service posts is generated based on the dynamically modified matching degree; The adaptation degree sequence is sorted and filtered to select an optimal adaptation combination, and the fusion decision data is generated based on the optimal adaptation combination.

7. The method for full-process digital management of volunteer service activities according to claim 1, characterized in that, The historical activity data is structured, and the behavior portrait is generated through a deep neural network based on the structured historical activity data and the real-time state data, including: Skill expertise information and service timeliness information are extracted from the historical activity data, and position trajectory data and physical consumption data are extracted from the real-time state data; The skill expertise information, the service timeliness information, the position trajectory data, and the physical consumption data are correspondingly processed to obtain first feature data, second feature data, third feature data, and fourth feature data; The first feature data, the second feature data, the third feature data, and the fourth feature data are input into a deep neural network, and a behavior portrait is generated through multi-level feature extraction and aggregation of the deep neural network.

8. A full-process digital management system for a volunteer service activity, characterized in that, Comprise: An acquisition module is configured to acquire historical activity data, real-time state data, environmental state information of a target activity area, and activity progress data during a volunteer service activity; A generation module is configured to perform structural processing on the historical activity data, and generate a behavior portrait based on the historical activity data after the structural processing, in combination with the real-time state data, through a deep neural network; An analysis module is configured to perform spatio-temporal dimension analysis on the environmental state information to form environmental assessment data; A fusion module is configured to perform multi-source feature fusion on the behavior portrait and the environmental assessment data by using a multi-source data fusion algorithm, and evaluate the adaptation degree of volunteer ability and environmental demand through dynamic matching degree calculation during the fusion process to generate fusion decision data of the volunteer service; A management module is configured to perform joint processing on the fusion decision data and the activity progress data by using a reinforcement learning decision model to generate a task allocation scheme, and perform dynamic management on the whole process of the volunteer service activity according to the task allocation scheme.

9. An electronic device, comprising: Comprise: A memory is configured to store a computer program; A processor is configured to implement the steps of the whole-process digital management method of the volunteer service activity according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program can implement the whole-process digital management method of the volunteer service activity according to any one of claims 1 to 7 when executed by the processor.

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