Potential field modeling and quantization method and system of information load under wide-area non-uniform scene

Abstract

The application provides a potential field modeling and quantization method and system for information load in a wide area non-uniform scene, and relates to the technical field of communication. The method comprises the following steps: constructing an information load model, calculating an information load value, forming a discrete load point set, then smoothing the discrete load point set into a spatially continuous distributed information load potential field based on a multi-scale adaptive kernel density estimation method, and performing normalization processing by using a real-time dynamic normalization method based on a sliding window. The method can accurately quantify discrete user information load and construct a smooth continuous spatial distribution model, has the advantages that the discrete user information load can be accurately quantified, a smooth continuous spatial distribution model is constructed, and real-time monitoring of battlefield information load and dynamic optimization configuration of communication resources are supported.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a potential field modeling and quantification method and system for information load in a wide-area non-uniform scenario. Background Technology

[0002] In large-scale, wide-area combat environments, the information transmission needs of ground users exhibit high complexity and dynamism. User types are diverse, including commanders, individual soldiers, vehicle-mounted platforms, and reconnaissance units, each with significantly different communication service requirements, encompassing various service forms such as command instructions, voice communication, location reporting, image transmission, and video streaming. These service requirements are highly time-varying, meaning they can fluctuate rapidly in a short period due to changes in the battlefield situation; simultaneously, they exhibit non-uniform spatial distribution, resulting in highly discrete and unevenly distributed information load across the battlefield area. Existing information load processing methods typically rely on uniform distribution assumptions or static models, which struggle to effectively address the challenges posed by multiple service overlaps, rapid time-varying characteristics, and differences in user types. Therefore, it is impossible to accurately quantify discrete user information loads or construct a smooth, continuous spatial distribution model, thus affecting real-time monitoring of battlefield information loads and the dynamic optimization of communication resource allocation.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] To accurately quantify discrete user information load and construct a smooth and continuous spatial distribution model, thereby supporting real-time monitoring of battlefield information load and dynamic optimization of communication resources, this application provides a potential field modeling and quantification method and system for information load in wide-area non-uniform scenarios.

[0005] Firstly, the potential field modeling and quantization method for information load in a wide-area non-uniform scene provided in this application adopts the following technical solution:

[0006] A potential field modeling and quantization method for information load in a wide-area non-uniform scene includes:

[0007] Construct an information load model oriented towards the information transmission needs of ground users in combat, calculate the information load value of each ground user at time t, and form a discrete load point set;

[0008] Based on the multi-scale adaptive kernel density estimation method, the discrete load point set is smoothed into a spatially continuous information load potential field;

[0009] The information load potential field is normalized by a real-time dynamic normalization method based on a sliding window, resulting in a normalized information load potential field.

[0010] Optionally, the step of constructing an information load model oriented towards the information transmission needs of ground users in combat, calculating the information load value of each ground user at time t, and forming a discrete load point set includes:

[0011] In the battlefield at a given time t Location of ground users and the type of each user The user type Selected from one of the following: commander, individual soldier, vehicle-mounted platform, or reconnaissance unit;

[0012] Determine the communication service set for each user i at time t. The communication services include command instructions, voice communication, location reporting, image transmission, and video streaming;

[0013] Calculate each business item In the time window Average effective information rate within ;

[0014] Introducing weighting coefficients Based on the average effective information rate and weighting coefficients Calculate the information load value of user i at time t. ;

[0015] Based on the location of all users and information load value This forms a discrete load point set. .

[0016] Optionally, the average effective information rate The calculation method is as follows:

[0017] When service m is a discrete-triggered service ;

[0018] in, , ≥1 represents the protocol and redundancy overhead coefficient corresponding to service m. This represents the number of times service m occurs within the time window [t-ΔT,t]. Let m be the amount of data generated in a single transaction; when m is a continuous stream transaction... It is given directly by the encoding bitrate of service m.

[0019] Optionally, information load value The formula for calculation is:

[0020]

[0021] in Used to reflect the command priority and resource occupancy sensitivity of business m.

[0022] Optionally, the step of constructing the information load potential field based on multi-scale adaptive kernel density estimation includes:

[0023] Adopt a Gaussian kernel function, and the expression of the Gaussian kernel function is:

[0024]

[0025] where is the kernel bandwidth corresponding to user i, ;

[0026] Based on the Gaussian kernel function and the discrete load point set, calculate the information load potential field .

[0027] Optionally, the determination method of the kernel bandwidth is: , where is the global reference bandwidth, is the average distance mean of the k-nearest neighbor users of all users, is the average distance of the k-nearest neighbor users of user i, and η∈(0,1] is the adjustment coefficient;

[0028] When user i is in a high-density area, < , such that <h0; when user i is an isolated and remote user, > , such that >h0.

[0029] Optionally, the information load potential field Φ(X(t)) satisfies: non-negativity Φ(X(t))≥0, boundedness Φ(X(t)) is a finite value, and the spatial integral satisfies , where Ω is the spatial region of the combat scenario.

[0030] Optionally, the specific process of real-time dynamic normalization is:

[0031] Set the sliding time window length to ΔT. At time t, collect the potential field values ρ(X,τ) corresponding to all spatial positions X∈Ω and all times τ∈[t - ΔT,t] within the window ;

[0032] Calculate the maximum value and the minimum value of the potential field values within the window;

[0033] Normalize the potential field at the current moment based on the maximum value and the minimum value, and the normalization formula is , where ε is the local minimum value used to avoid the denominator being 0;

[0034] Every time step δt elapses, the window data is updated using a rolling update method, discarding data in the interval [t-ΔT-δt, t-ΔT], introducing new data in the current δt interval, and updating the data using a recursive formula. and .

[0035] Optionally, the normalized information load potential field value ∈[0,1], and assign each grid region to... Mapping to colors, constructing a color mapping table, and forming a heat map of battlefield information load hotspot distribution.

[0036] Secondly, this application provides a potential field modeling and quantization system for information load in a wide-area non-uniform scene, comprising:

[0037] The model building module is used to build an information load model for the information transmission needs of combat ground users, calculate the information load value of each ground user at time t, and form a discrete load point set.

[0038] The information load potential field transformation module is used to smooth the discrete load point set into a spatially continuous information load potential field based on the multi-scale adaptive kernel density estimation method.

[0039] The output module is used to normalize the information load potential field using a real-time dynamic normalization method based on a sliding window, so as to obtain the normalized information load potential field.

[0040] In summary, this application constructs an information load model, calculates the information load value, and forms a discrete load point set. Then, based on a multi-scale adaptive kernel density estimation method, it smooths the discrete load point set into a spatially continuous information load potential field. Furthermore, it employs a real-time dynamic normalization method based on a sliding window for normalization processing. This approach can accurately quantify discrete user information loads and construct a smooth, continuous spatial distribution model. It has the advantages of accurately quantifying discrete user information loads and constructing a smooth, continuous spatial distribution model, thereby supporting real-time monitoring of battlefield information loads and dynamic optimization of communication resource allocation. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the first embodiment of the potential field modeling and quantization method for information load in a wide-area non-uniform scenario of this application;

[0042] Figure 2 This is a schematic diagram illustrating the construction of the information load potential field using multi-scale adaptive kernel density estimation in the first embodiment of the potential field modeling and quantification method for information load in wide-area non-uniform scenarios presented in this application. Figure 2 (a) is a diagram showing the results of adaptive kernel density estimation. Figure 2 (b) shows the surface effect diagram of a continuous potential field;

[0043] Figure 3 This is a structural block diagram of the first embodiment of the potential field modeling and quantization system for information load in a wide-area non-uniform scene according to this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] In wide-area, large-scale combat scenarios, the information transmission needs of ground users are characterized by multiple overlapping services, strong time-varying characteristics, and significant differences in user types. Traditional methods struggle to accurately discretize and quantify the information load of a single ground user at a given moment when dealing with these complex information needs. This makes it difficult to form a unified scalar expression, which in turn affects the subsequent construction of a continuous information load potential field and fails to effectively reflect the dynamic distribution of battlefield information load.

[0046] To address this, embodiments of this application provide a method for potential field modeling and quantization of information load in wide-area non-uniform scenarios, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the potential field modeling and quantization method for information load in a wide-area non-uniform scenario according to this application.

[0047] In this embodiment, the potential field modeling and quantization method for information load in a wide-area non-uniform scene includes the following steps:

[0048] Step S10: Construct an information load model for the information transmission needs of ground users in combat operations, calculate the information load value of each ground user at time t, and form a discrete load point set.

[0049] For ease of understanding, the following explains some key terms in this embodiment:

[0050] The information load model is used to describe and quantify the information transmission needs of ground users in combat at a specific moment. This model considers factors such as user type, communication service type, and traffic volume, aiming to transform complex and diverse information needs into calculable numerical values.

[0051] Information load is a measure of the intensity or importance of information transmission demand borne by a single ground user at a specific time t. This value is the output of the information load model and is used to compare and analyze the load of different users.

[0052] The discrete load point set consists of the locations of all ground users on the battlefield and their corresponding information load values. This point set is a discrete spatial representation of the information load, providing the basic data for subsequently constructing a continuous potential field.

[0053] A multi-scale adaptive kernel density estimation method is a statistical approach used to smooth discrete data points into a continuous density distribution. In this application, this method is applied to transform a discrete set of load points into a spatially continuous information load potential field, capable of adjusting the smoothing degree according to the local data density.

[0054] The information load potential field is a continuous distribution representation of information load in the battlefield space. This potential field is obtained by spatially smoothing discrete user information load values ​​and can intuitively reflect the information load intensity in different areas, similar to the concept of a potential field in physics.

[0055] The sliding window real-time dynamic normalization method is a data processing technique used to perform real-time statistical analysis and normalization on data within a dynamically changing data stream by setting a time window. This method ensures the dynamic adaptability of the normalization results and avoids errors that may be introduced by static normalization.

[0056] The normalized information load potential field is the result of mapping the value range of the original information load potential field to a specific range (e.g., [0,1]). This process helps to eliminate the influence of different dimensions or ranges, making the potential field values ​​more comparable and facilitating subsequent analysis and visualization.

[0057] It should be noted that the step of constructing an information load model oriented towards the information transmission needs of ground users in combat, calculating the information load value of each ground user at time t, and forming a discrete load point set includes: determining the information load value of each ground user at time t on the battlefield. Location of ground users and the type of each user The user type Select from one of the following: commander, individual soldier, vehicle-mounted platform, or reconnaissance unit; determine the communication service set for each user i at time t. The communication services include command instructions, voice communication, location reporting, image transmission, and video streaming; each service item is calculated. In the time window Average effective information rate within Introducing weighting coefficients Based on the average effective information rate and weighting coefficients Calculate the information load value of user i at time t. Based on the location of all users and information load value This forms a discrete load point set. .

[0058] It is understandable that the average effective information rate The calculation method is as follows:

[0059] When service m is a discrete-triggered service ;

[0060] in, , ≥1 represents the protocol and redundancy overhead coefficient corresponding to service m. This represents the number of times service m occurs within the time window [t-ΔT,t]. Let m be the amount of data generated in a single transaction; when m is a continuous stream transaction... It is given directly by the encoding bitrate of service m.

[0061] In practical implementation, the information load value The formula for calculation is:

[0062]

[0063] in Used to reflect the command priority and resource consumption sensitivity of business m.

[0064] In practical implementation, this embodiment, through the aforementioned technical solution, can fully consider the heterogeneity of ground users in terms of type, location, and communication service requirements. By meticulously determining user types and communication service sets, and combining the average effective information rate of the services with weighting coefficients reflecting command priority and resource occupancy sensitivity, the information load value of each user at a specific moment can be accurately quantified. This refined modeling approach enables the resulting discrete load point set to more realistically and comprehensively reflect the actual distribution and importance of battlefield information needs, providing high-quality input data for subsequent information load potential field smoothing and normalization processing, thereby significantly improving the accuracy and practicality of information load modeling in wide-area non-uniform scenarios.

[0065] Step S20: Based on the multi-scale adaptive kernel density estimation method, smooth the discrete load point set into a spatially continuously distributed information load potential field.

[0066] It is understandable that the information load potential field is a scalar field continuously distributed in space, and its value represents the intensity of information transmission demand in the vicinity of that location. The steps for constructing the information load potential field based on multi-scale adaptive kernel density estimation include: using a Gaussian kernel function, the expression of which is:

[0067]

[0068] wherein is the kernel bandwidth corresponding to user i, ;

[0069] Based on the Gaussian kernel function and the discrete load point set, calculate the information load potential field .

[0070] It can be understood that, due to the highly uneven distribution of users in the wide-area scenario, it is difficult for a kernel function with a single bandwidth to balance local details and overall trends. In this embodiment, an adaptive multi-scale kernel density estimation method is introduced to adjust the kernel bandwidth according to the local user density.

[0071] It should be noted that the determination method of the kernel bandwidth is as follows: , wherein is the global reference bandwidth, is the average value of the average distances of the k-nearest neighbor users of all users, is the average distance of the k-nearest neighbor users of user i, and η∈(0,1] is the adjustment coefficient;

[0072] When user i is in a high-density area, < , such that <h0; when user i is an isolated and remote user, > , such that >h0.

[0073] It can be understood that the information load potential field Φ(X(t)) satisfies: non-negativity Φ(X(t))≥0, boundedness Φ(X(t)) is a finite value, and the spatial integral satisfies , where Ω is the spatial region of the combat scenario. That is, the spatial integral of the potential field strength is equal to the total information load. )'s gradient ) indicates the direction in which the load intensity increases fastest and can be used to guide the UAV to move towards the high-demand area.

[0074] Such as Figure 2 shown in the schematic diagram of constructing the information load potential field by multi-scale adaptive kernel density estimation. Wherein Figure 2 (a) represents: the visualization of the spatial distribution of ground users based on adaptive kernel density estimation. Through circular kernel functions of different scales and colors, the spatial aggregation characteristics of ground users are intuitively presented. The red dense area represents the high-density aggregation area of users, and the blue area corresponds to the low-density or other types of node distributions, clearly depicting the heterogeneity of the user spatial density. Figure 2(b) represents the continuous information load potential field surface generated by the adaptive kernel density estimation result. The potential field intensity is characterized by a three-dimensional curved surface combined with color gradients (red / yellow / green / blue). The red and yellow peaks correspond to the high potential field in the high-density area of ​​the user, and the blue valleys correspond to the low potential field in the low-density area, which intuitively shows the spatial shape and intensity distribution law of the information load potential field.

[0075] This embodiment enables a more refined and accurate quantification of the information load value of user i at time t. By introducing weighting coefficients and applying them to the calculation of the average effective information rate, the command priority and resource consumption sensitivity of different services can be fully considered when integrating user communication service information. For example, for services with high command priority or high resource consumption sensitivity, their weighting coefficients can be set higher, thus occupying a more prominent position in the information load model and accurately reflecting their actual demand for network resources and their criticality to combat operations. This avoids the evaluation bias that may be caused by simply accumulating or averaging service rates, making the potential field modeling of information load closer to the actual battlefield needs. This provides more accurate and reliable basic data for subsequent dynamic allocation of communication resources, network congestion prediction, and optimization decisions, thereby improving the practicality and guidance of information load modeling in wide-area non-uniform scenarios.

[0076] Step S30: The information load potential field is normalized using a real-time dynamic normalization method based on a sliding window to obtain the normalized information load potential field.

[0077] Understandably, this embodiment proposes a multi-scale adaptive kernel density estimation method to smooth a discrete load point set into a spatially continuous information load potential field. However, in its implementation, how to select a suitable kernel function and how to effectively transform the discrete point set into a continuous potential field based on the kernel function are key technical issues to ensure the accuracy and practicality of the potential field model. If the kernel function is not properly selected or the potential field calculation method is unclear, the potential field may fail to accurately reflect the regional distribution characteristics of the information load, affecting subsequent situational awareness and resource scheduling.

[0078] It should be noted that the specific process of real-time dynamic normalization is as follows: The sliding time window length is set to ΔT, and at time t, the collection window... Find the potential field value ρ(X,τ) corresponding to all spatial locations X∈Ω and all times τ∈[t-ΔT,t] within the window; calculate the maximum value of the potential field value within the window. and minimum value ;

[0079] The potential field at the current moment is normalized based on the maximum and minimum values, and the normalization formula is as follows: , where ε is the local minimum value used to avoid the denominator being 0;

[0080] Every time step δt elapses, the window data is updated using a rolling update method, discarding data in the interval [t-ΔT-δt, t-ΔT], introducing new data in the current δt interval, and updating the data using a recursive formula. and .

[0081] This embodiment can smooth a discrete set of user information load points into a spatially continuous information load potential field in a mathematically rigorous manner that conforms to actual physical attenuation laws. The introduction of the Gaussian kernel function avoids the discontinuities or inaccuracies that may arise from simple interpolation, making the potential field distribution more realistically reflect the regional impact of information load. Simultaneously, by using the information load value of each user as a weight for superposition calculation, it ensures that the potential field can comprehensively reflect the load contribution of all users, thus forming a continuous and quantifiable information load distribution map. This provides accurate and intuitive basis for subsequent battlefield situational awareness, communication resource optimization, and decision support, significantly improving the accuracy and practicality of information load modeling.

[0082] It is understandable that the normalized information load potential field value ∈[0,1], and assign each grid region to... Mapping to colors, constructing a color mapping table, and forming a heat map of battlefield information load hotspot distribution.

[0083] By mapping the normalized information load potential field values ​​to colors and constructing a color mapping table, a heatmap of battlefield information load hotspot distribution is ultimately formed. This effectively solves the problem that combat commanders find it difficult to quickly identify the battlefield information load distribution from numerical data. This visualization method transforms abstract numerical data into intuitive graphical information, enabling commanders to immediately identify information load hotspots on the battlefield—areas with high information transmission demand, potential communication congestion, or resource shortages. This significantly improves the efficiency and accuracy of battlefield situational awareness, helping commanders quickly assess the current pressure distribution of the communication network. This allows for timely adjustments to communication resource allocation, optimization of information flow paths, or other intervention measures to ensure the smooth transmission of critical information and enhance the timeliness and effectiveness of operational decisions.

[0084] It should be noted that the advantages of this embodiment are:

[0085] To address the uneven spatial distribution and dynamic changes in ground user information transmission demands within combat environments, this paper proposes a potential field modeling and quantification method for information load in wide-area non-uniform scenarios. The ground user information transmission demands driven by combat missions are transformed into a quantifiable, time-varying, and spatially mappable information load model, thus providing a theoretical foundation for the construction and quantitative analysis of the information load potential field. A multi-scale adaptive kernel density estimation method is employed to transform the discrete ground user information load into a smooth and analyzable spatial information load distribution, accurately reflecting the spatial characteristics and dynamic changes of the information load. Furthermore, a real-time dynamic normalization method based on a sliding window is proposed to update the information load potential field model in real time when facing dynamic combat situations, ensuring efficient resource scheduling and mission execution capabilities in a constantly changing environment.

[0086] This embodiment constructs an information load model, calculates the information load value, and forms a discrete load point set. Then, based on a multi-scale adaptive kernel density estimation method, it smooths the discrete load point set into a spatially continuous information load potential field. It then uses a real-time dynamic normalization method based on a sliding window for normalization processing. This allows for the accurate quantification of discrete user information load and the construction of a smooth, continuous spatial distribution model. It has the advantages of accurately quantifying discrete user information load and constructing a smooth, continuous spatial distribution model, thereby supporting real-time monitoring of battlefield information load and dynamic optimization of communication resource allocation.

[0087] Reference Figure 3 , Figure 3 This is a structural block diagram of the first embodiment of the potential field modeling and quantization system for information load in a wide-area non-uniform scene according to this application.

[0088] like Figure 3 As shown in the embodiments of this application, the potential field modeling and quantization system for information load in a wide-area non-uniform scene includes:

[0089] Model building module 10 is used to build an information load model for the information transmission needs of ground users in combat, calculate the information load value of each ground user at time t, and form a discrete load point set.

[0090] The information load potential field transformation module 20 is used to smooth the discrete load point set into a spatially continuous information load potential field based on the multi-scale adaptive kernel density estimation method.

[0091] The output module 30 is used to normalize the information load potential field using a real-time dynamic normalization method based on a sliding window, so as to obtain the normalized information load potential field.

[0092] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solution of this application. In specific applications, those skilled in the art can make settings as needed, and this application does not impose any restrictions on this.

[0093] This embodiment constructs an information load model, calculates the information load value, and forms a discrete load point set. Then, based on a multi-scale adaptive kernel density estimation method, it smooths the discrete load point set into a spatially continuous information load potential field. It then uses a real-time dynamic normalization method based on a sliding window for normalization processing. This allows for the accurate quantification of discrete user information load and the construction of a smooth, continuous spatial distribution model. It has the advantages of accurately quantifying discrete user information load and constructing a smooth, continuous spatial distribution model, thereby supporting real-time monitoring of battlefield information load and dynamic optimization of communication resource allocation.

[0094] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0095] In addition, for technical details not described in detail in this embodiment, please refer to the method for potential field modeling and quantization of information load in wide-area non-uniform scenarios provided in any embodiment of this application, which will not be repeated here.

[0096] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0097] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. All equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for potential field modeling and quantization of information load in a wide-area non-uniform scene, characterized in that, include: Construct an information load model oriented towards the information transmission needs of ground users in combat, calculate the information load value of each ground user at time t, and form a discrete load point set; Based on the multi-scale adaptive kernel density estimation method, the discrete load point set is smoothed into a spatially continuous information load potential field; The information load potential field is normalized by a real-time dynamic normalization method based on a sliding window to obtain the normalized information load potential field. The step of constructing an information load model oriented towards the information transmission needs of ground users in combat, calculating the information load value of each ground user at time t, and forming a discrete load point set includes: In the battlefield at a given time t Location of ground users and the type of each user The user type Selected from one of the following: commander, individual soldier, vehicle-mounted platform, or reconnaissance unit; Determine the communication service set for each user i at time t. Communication services include command and control, voice communication, location reporting, image transmission, and video streaming; Calculate each business item In the time window Average effective information rate within ; Introducing weighting coefficients Based on the average effective information rate and weighting coefficients Calculate the information load value of user i at time t. ; Based on the location of all users and information load value This forms a discrete load point set. ; The steps for constructing the information load potential field based on multi-scale adaptive kernel density estimation include: A Gaussian kernel function is used, and the expression of the Gaussian kernel function is: in For user i, the core bandwidth is... ; Based on the Gaussian kernel function and the discrete load point set, calculate the information load potential field. ; The specific process of real-time dynamic normalization is as follows: Set the sliding time window length to ΔT, and at time t, collect the window... The potential field value ρ(X,τ) corresponding to all spatial locations X∈Ω and all times τ∈[t-ΔT,t]; Calculate the maximum value of the potential field within the window. and minimum value ; The potential field at the current moment is normalized based on the maximum and minimum values, and the normalization formula is as follows: , where ε is the local minimum value used to avoid the denominator being 0; Every time step δt elapses, the window data is updated using a rolling update method, discarding data in the interval [t-ΔT-δt, t-ΔT], introducing new data in the current δt interval, and updating the data using a recursive formula. and .

2. The method for potential field modeling and quantization of information load in a wide-area non-uniform scene according to claim 1, characterized in that, The average effective information rate The calculation method is as follows: When service m is a discrete-triggered service ; in, , ≥1 represents the protocol and redundancy overhead coefficient corresponding to service m. This represents the number of times service m occurs within the time window [t-ΔT,t]. Let m be the amount of data generated in a single transaction; when m is a continuous stream transaction... It is given directly by the encoding bitrate of service m.

3. The method for potential field modeling and quantization of information load in a wide-area non-uniform scene according to claim 1, characterized in that, Information load value The formula for calculation is: in Used to reflect the command priority and resource consumption sensitivity of business m.

4. The method for potential field modeling and quantization of information load in a wide-area non-uniform scene according to claim 1, characterized in that, nuclear bandwidth The method for determining it is as follows: ,in As the global baseline bandwidth, Let k be the mean distance between all users' k nearest neighbors. Let be the average distance of user i's k nearest neighbors, and η∈(0,1] be the adjustment coefficient; When user i is in a high-density area, < , such that < h0; when user i is an isolated and remote user, > , such that > h0.

5. The method for potential field modeling and quantization of information load in a wide-area non-uniform scene according to claim 1, characterized in that, The information load potential field Φ(X(t)) satisfies: nonnegativity Φ(X(t))≥0, boundedness Φ(X(t)) is a finite value, and the spatial integral satisfies , where Ω represents the spatial region of the combat scenario.

6. The method for potential field modeling and quantization of information load in a wide-area non-uniform scene according to claim 1, characterized in that, Normalized information load potential field value ∈[0,1], and assign each grid region to... Mapping to colors, constructing a color mapping table, and forming a heat map of battlefield information load hotspot distribution.

7. A potential field modeling and quantization system for information load in a wide-area non-uniform scene, characterized in that, Performing the method as described in claim 1 includes: The model building module is used to build an information load model for the information transmission needs of combat ground users, calculate the information load value of each ground user at time t, and form a discrete load point set. The information load potential field transformation module is used to smooth the discrete load point set into a spatially continuous information load potential field based on the multi-scale adaptive kernel density estimation method. The output module is used to normalize the information load potential field using a real-time dynamic normalization method based on a sliding window, so as to obtain the normalized information load potential field.

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