Internet of Things terminal one-key deployment software system and method based on embedded Web server

Abstract

The invention discloses an Internet of Things terminal one-key deployment software system and method based on an embedded Web server, and relates to the field of digital information transmission. In an initialization stage of establishing connection between the Web server and a mobile terminal, a reflection interference physical field based on entity geometric projection is solved, a channel fading probability distribution diagram is output, and a channel fading probability distribution diagram is calculated; frame error rate coupling analysis based on Bayesian inference and extreme value optimization based on an effective throughput expectation function are executed, a physical layer transmission collapse critical value is solved, and an application layer maximum safety load threshold value is output; the method comprises the following steps: executing application layer active physical cutting on a static resource binary stream required by page deployment, generating an application layer micro-frame data queue, resolving a dynamic network congestion index based on the application layer micro-frame data queue, driving a dual-threshold segmented flow control strategy to adaptively adjust a micro-frame sending beat, and outputting a one-key deployment page. And it is ensured that the one-key deployment page can still be completely rendered under extreme working conditions such as metal rod shading.

Description

Technical Field

[0001] This invention relates to the field of digital information transmission, specifically to a software system and method for one-click deployment of IoT terminals based on an embedded web server. Background Technology

[0002] In the construction of smart city IoT infrastructure, smart street light controllers based on the NEMA standard interface are typically deployed on top of metal light poles. In on-site maintenance scenarios with such embedded web servers, maintenance terminals (such as mobile handheld devices) need to establish a connection with the controller via short-range wireless protocols such as IEEE 802.11 (Wi-Fi) to load and configure HTML / JS static resources. However, because the metal light pole itself forms a vertically erected, well-conducting cylindrical surface, a strong multipath specular reflection effect occurs when wireless radio frequency signals (especially in the 2.4GHz band) propagate between the metal cover of the light fixture and the side of the pole. According to electromagnetic wave propagation theory, direct and reflected waves will undergo phase reversal and superposition at specific spatial locations, leading to Rayleigh fading or frequency-selective fading, thus forming several spatial pits with drastically reduced signal strength in the near-field region centered on the light pole. This physical channel impairment caused by the rigid geometric structure results in severe signal-to-noise ratio fluctuations in the communication link within a very short displacement range.

[0003] In the aforementioned harsh physical channel environments, existing embedded TCP / IP protocol stacks typically use standard Ethernet maximum transmission units (MTBUs) for data encapsulation. While the link can be maintained during the initial TCP three-way handshake or lightweight HTTP status queries (small packet interactions) due to the short data frames and relatively controllable frame error rate, when maintenance personnel attempt to load a one-click deployment page containing large HTML / JS / CSS files, the web server sends a full-load of long data frames according to the maximum segment length. In areas with instantaneous high bit error rates caused by multipath fading, the data frame length and frame error rate exhibit a non-linear positive correlation. The probability of long frames failing physical layer verification increases exponentially, causing the wireless link layer to fall into a dead loop of sending-failure-backoff-retransmission. This phenomenon, where the physical layer's long frame transmission capacity collapses while the upper-layer protocol fails to adaptively sense and adjust the payload, can cause the TCP connection to enter a black hole state. Specifically, this manifests as HTTP progress bars stalling and complex pages failing to render, severely restricting the success rate of on-site delivery of IoT terminals.

[0004] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0005] To address the technical problems raised in the background section, this invention is proposed. This invention provides a one-click deployment software system and method for IoT terminals based on an embedded web server.

[0006] This invention is achieved through the following technical solution:

[0007] A method for one-click software deployment on an IoT terminal based on an embedded web server includes the following steps:

[0008] During the initialization phase of establishing a connection between the web server and the mobile device, based on the geometric positional relationship of the NEMA controller relative to the metal lamp post, the physical field of reflection interference based on the solid geometric projection is calculated, and the channel fading probability distribution map is output.

[0009] The channel fading probability distribution map and the real-time collected wireless link layer retransmission count are used as dual inputs to perform frame error rate coupling analysis based on Bayesian inference and extreme value optimization based on the expected function of effective throughput. The physical layer transmission collapse threshold is calculated and the maximum safe load threshold of the application layer is output after safety backoff correction.

[0010] Based on the maximum security load threshold of the application layer, the application layer actively performs physical segmentation on the static resource binary stream required for the deployment page, adds an auto-incrementing sequence number protocol header to the independent logical data blocks obtained from the segmentation, and generates an application layer microframe data queue.

[0011] Based on the application-layer microframe data queue, the dynamic network congestion index is calculated, driving the dual-threshold segmented flow control strategy to adaptively adjust the microframe sending rhythm, and outputting a one-click deployment page.

[0012] Furthermore, the step of generating the output channel fading probability distribution map includes:

[0013] Obtain the relative spatial coordinates of the mobile device and the NEMA controller, combine them with the physical size parameters of the metal light pole, perform geometric optical path construction based on cylindrical projection dimensionality reduction and virtual image unfolding, and output the physical phase difference set;

[0014] Traverse the set of physical phase differences, mark the discrete sampling points where the total physical phase difference falls into the reverse cancellation decision interval centered on π as instantaneous signal pits, and generate pit distribution data.

[0015] Furthermore, the step of generating the output channel fading probability distribution map further includes:

[0016] The fading hit probability of each discrete sampling point in the deep pit distribution data of multiple consecutive frames is statistically analyzed. The discrete sampling points with fading hit probabilities higher than the dynamic and static determination threshold are locked as constant fading intervals, and the channel fading probability distribution map is generated.

[0017] Furthermore, the step of constructing the geometric optical path based on cylindrical projection dimensionality reduction and virtual mirror unfolding includes:

[0018] Establish a cylindrical coordinate system with the central axis of the metal lamp post as the Z-axis, and project the physical transmitter point of the NEMA controller and the physical receiver point of the mobile terminal onto the same horizontal reference plane along the Z-axis to obtain a two-dimensional projection point pair;

[0019] On the horizontal reference plane, a physical blocking boundary is constructed with the radius of the metal lamp post. Based on Fermat's principle of shortest optical path, a unique reflection tangent point that satisfies the constraint that the incident angle equals the reflection angle is searched on the physical blocking boundary using a geometric construction method.

[0020] Furthermore, the step of constructing the geometric optical path based on cylindrical projection dimensionality reduction and virtual mirror unfolding also includes:

[0021] A tangent plane is constructed at the unique reflection tangent point, and the tangent plane is defined as an instantaneous local reflection mirror.

[0022] Construct a virtual mirror image of the transmitting projection point in the two-dimensional projection point pair with respect to the instantaneous local reflecting mirror surface, connect the virtual mirror image point and the receiving projection point in the two-dimensional projection point pair to form an equivalent straight reflection path, and connect the transmitting projection point and the receiving projection point to form a physical direct reflection path.

[0023] Furthermore, the step of constructing the geometric optical path based on cylindrical projection dimensionality reduction and virtual mirror unfolding also includes:

[0024] The geometric path difference is obtained by calculating the Euclidean distance difference between the equivalent straight reflection path and the physical direct path;

[0025] By combining the electromagnetic wave wavelength parameters, the geometric path difference is converted into the total physical phase difference, and the set of physical phase differences is output. The total physical phase difference includes the propagation delay phase lag and the reflection phase abrupt change.

[0026] Furthermore, the step of outputting the maximum safe load threshold for the application layer includes:

[0027] The channel fading probability distribution map is received, and combined with the current wireless link layer retransmission count, a nonlinear mapping relationship between packet length and frame error rate is established through Bayesian inference logic, and a packet length-frame error rate coupling curve is output.

[0028] Construct an effective throughput expectation function based on the packet length-frame error rate coupling curve;

[0029] The effective throughput expectation function is differentiated and analyzed to find the critical inflection point where the effective throughput drops sharply, and the critical value of physical layer transmission collapse is output.

[0030] The physical layer transmission collapse threshold is adjusted downward by a preset security backoff factor, and the TCP / IP protocol header overhead is deducted to obtain the application layer maximum security load threshold.

[0031] Furthermore, the step of generating the application layer microframe data queue includes:

[0032] Read the static resource binary stream to be sent from the Web server memory, and physically cut the binary stream into several independent logical data blocks with the application layer's maximum security load threshold as the cutting step size;

[0033] A protocol header containing an auto-incrementing sequence number is added to each independent logical data block, and microframe units are encapsulated to generate the application layer microframe data queue.

[0034] Furthermore, the step of outputting the one-click deployment page includes:

[0035] The application layer microframe data queue is traversed, microframes are written into the kernel buffer and sent, and transmission characteristics are statistically analyzed in real time based on the application layer confirmation messages fed back by the mobile terminal, and a dynamic network congestion index is output.

[0036] The dynamic network congestion index is compared with the idle threshold and the congestion threshold. Segmented flow control is performed, including full-speed transmission, linear adjustment and forced avoidance. The microframe transmission interval is adjusted in real time until all microframes are confirmed to be received, and a one-click deployment page is output.

[0037] A one-click deployment software system for IoT terminals based on an embedded web server includes:

[0038] The channel fading module is used during the initialization phase of establishing a connection between the web server and the mobile device. Based on the geometric positional relationship between the NEMA controller and the metal lamp post, it calculates the reflection interference physical field based on the solid geometric projection and outputs a channel fading probability distribution map.

[0039] The security payload module is used to take the channel fading probability distribution map and the real-time collected wireless link layer retransmission count as dual inputs, perform frame error rate coupling analysis based on Bayesian inference and extreme value optimization based on the expected function of effective throughput, calculate the physical layer transmission collapse threshold, and output the application layer maximum security payload threshold after security backoff correction.

[0040] The microframe queue module is used to perform application-layer active physical segmentation on the static resource binary stream required for the deployment page based on the application layer maximum security load threshold, add an auto-incrementing sequence number protocol header to the segmented independent logical data blocks, and generate an application layer microframe data queue.

[0041] The flow control transmission module is used to calculate the dynamic network congestion index based on the application layer microframe data queue, drive the dual-threshold segmented flow control strategy to adaptively adjust the microframe sending rhythm, and output a one-click deployment page.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] In the initialization phase of establishing a connection between the web server and the mobile terminal, this invention calculates the reflection interference physical field based on the geometric positional relationship of the NEMA controller relative to the metal lamppost, outputting a channel fading probability distribution map. Using this channel fading probability distribution map and real-time collected wireless link layer retransmission counts as dual inputs, it performs Bayesian inference-based frame error rate coupling analysis and extremum optimization based on the expected function of effective throughput to calculate the physical layer transmission collapse threshold. After safety backoff correction, it outputs the application layer maximum safe load threshold. Based on the application layer maximum safe load threshold, it performs application layer active physical segmentation on the static resource binary stream required for deploying the page, adding an auto-incrementing sequence number protocol header to the segmented independent logical data blocks to generate an application layer microframe data queue. By establishing a reflection interference physical field model based on physical geometric projection, combined with Bayesian inference and extremum optimization of the expected function of effective throughput, it is possible to accurately quantify and lock the instantaneous signal pits and physical layer transmission collapse thresholds in space before data transmission. This mechanism fundamentally corrects the shortcomings of traditional IoT devices that ignore the physical channel carrying capacity limit and blindly use the standard Ethernet MTU for transmission. It ensures that the system always operates within a safe load range that is less than the inflection point of the channel frame error rate, effectively preventing link layer avalanche packet loss caused by the data packet length exceeding the physical layer coherence bandwidth.

[0044] This invention, based on an application-layer microframe data queue, calculates a dynamic network congestion index and drives a dual-threshold segmented flow control strategy to adaptively adjust the microframe transmission rhythm, outputting a one-click deployment page. Through application-layer active physical segmentation and a dual-threshold segmented flow control strategy, a microframe transmission and asynchronous detection system independent of the operating system kernel is constructed. This system bypasses the erroneous exponential backoff mechanism of the TCP protocol stack in the event of bit errors and packet loss. It utilizes real-time RTT feedback to drive the transmission rhythm to adaptively switch between full-speed, linear, and avoidance intervals, thereby transforming uncontrollable connection timeout problems into controllable low-speed stable transmission. This ensures that the one-click deployment page can still be fully rendered even under extreme conditions such as metal pole obstruction, significantly improving the success rate and stability of IoT terminal field delivery. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not drawn to scale according to the actual size, but are intended to show the main idea of ​​the present invention.

[0046] Figure 1 A flowchart illustrating a method for one-click software deployment on an IoT terminal based on an embedded web server;

[0047] Figure 2 A system block diagram for a one-click deployment software system for IoT terminals based on an embedded web server;

[0048] Figure 3 This is a schematic diagram of the mapping relationship from three-dimensional physical space to a two-dimensional projection plane.

[0049] Figure 4 This is a schematic diagram of reflection path construction and equivalent expansion based on the virtual mirror method. Detailed Implementation

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are also within the scope of protection of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, a method for one-click software deployment on an IoT terminal based on an embedded web server includes the following steps:

[0053] Step S100: During the initialization phase of establishing a connection between the Web server and the mobile terminal, based on the geometric positional relationship of the NEMA controller relative to the metal lamp post, the reflection interference physical field based on the solid geometric projection is calculated, and the channel fading probability distribution map is output.

[0054] In step S100, the initialization phase of establishing a connection between the web server and the mobile terminal refers to the preparatory silent period before the mobile terminal (such as the installer's mobile phone) formally initiates an HTTP GET request to deploy a complex page (containing large HTML / JS / CSS resources) using a one-click method. During this phase, the system has not yet injected a large amount of service load into the channel. At this time, the embedded web server utilizes CPU idle time to perform physical field reconstruction based on preset structural parameters, performing pre-calculation before transmission.

[0055] The process of outputting the channel fading probability distribution map in step S100 specifically includes the following steps:

[0056] Step S101: Obtain the relative spatial coordinates of the mobile terminal and the NEMA controller, combine them with the physical size parameters of the metal lamp post, perform geometric optical path construction based on cylindrical projection dimensionality reduction and virtual mirror unfolding, and output the physical phase difference set;

[0057] Step S1011: Establish a cylindrical coordinate system with the central axis of the metal lamp post as the Z-axis, and project the physical transmitter point of the NEMA controller and the physical receiver point of the mobile terminal along the Z-axis onto the same horizontal reference plane to obtain a two-dimensional projection point pair;

[0058] like Figure 3 The diagram shows the mapping relationship between three-dimensional physical space and a two-dimensional projection plane.

[0059] Reference Figure 3 The diagram shows that Z (axis) is the positive direction of the coordinate system established with the vertically upward central axis of the metal lamp post, represented by a solid black arrow; O is the geometric center origin of the cross-section of the metal lamp post; (The suspended blue sphere) is the physical emission point at the center of the NEMA controller antenna, representing the radiation source of electromagnetic wave energy; illustration. (The suspended blue sphere) is the physical receiving point at the center of the mobile antenna, representing the actual location of the observation device; illustration. and (Green dashed lines) represent the vertical height and projected trajectory of the transmitting and receiving points relative to the horizontal reference plane, respectively; illustration. and (Hollow dots on the ground) represent the transmit and receive projection points obtained after dimensionality reduction projection; illustration (The black solid line connecting the two projection points represents the horizontal projected Euclidean distance between them.)

[0060] In step S1011, refer to Figure 3 This is a schematic diagram of cylindrical projection and dimensionality reduction of a three-dimensional spatial entity provided in this embodiment. In the initial stage of solving the reflection interference physical field based on the geometric projection of the entity, a system is established with the central axis of the metal lamp post as... A cylindrical coordinate system for the axes. The specific method for establishing this system is as follows: Figure 3 As shown, the origin is the geometric center of the cross-section of the metal lamp post. With the vertically upward center axis of the lamp post as the direction Positive axis direction Figure 3 The black solid arrow pointing upwards points to the polar axis (corresponding to the polar angle) with any chosen radial direction on the horizontal plane as the polar axis. In this coordinate system, obtain the physical transmission point of the NEMA controller antenna center. The three-dimensional coordinates, and the physical receiving point at the center of the mobile antenna. The three-dimensional coordinates. Figure 3In this model, these two physical points are represented by blue spheres suspended in the air. The physical transmission point at the center of the NEMA controller antenna refers to the antenna phase center of the wireless radio frequency module (such as a Wi-Fi module) inside the NEMA controller. It represents the geometric starting point of electromagnetic wave energy radiation outward and is considered a point wave source in the physical model. The physical receiving point at the center of the mobile terminal antenna refers to the geometric center of the mobile terminal's receiving antenna. It represents the actual location of the observation point in three-dimensional space. The mobile terminal is such as a handheld smart device used by the operator. This represents the radial distance of the NEMA controller's transmitting antenna relative to the central axis of the metal lamppost, i.e., from the transmitting point to... The perpendicular distance of the axis; Represents the azimuth angle of the launch point projected onto the horizontal plane, and the rotation angle relative to the polar axis; Represents the vertical height of the launch point relative to the horizontal plane where the origin is located, such as Figure 3 Zhongyou The length of the downward-pointing green dashed line is indicated by its length. This represents the radial distance of the mobile receiving antenna relative to the central axis of the metal lamppost, i.e., the distance from the receiving point to the center line of the lamppost. The perpendicular distance of the axis; This represents the azimuth angle of the receiving point projected onto the horizontal plane. Represents the vertical height of the receiving point relative to the horizontal plane where the origin is located, corresponding to Figure 3 The height of the green dashed line on the right side. Since NEMA controllers are typically mounted above the luminaire using a standard NEMA base, its radial distance... and vertical height These are known fixed values ​​or prior parameters that can be obtained from structural drawings; the coordinates of the mobile device. It can be calculated by combining GPS / BeiDou positioning data reported by the receiving end with barometric altimeter data, or by calibrating with a laser rangefinder in a static deployment scenario.

[0061] Execution along The projection operation of the axis projects the physical launch point in three-dimensional space. With physical receiving point Mapped to the same horizontal reference plane, for example The plane is represented by z, which is the vertical height. For specific implementation details, please refer to [reference needed]. Figure 3 The green projection trajectory line in the image maintains a radial distance. and azimuth Keep it unchanged, and force its vertical height component to zero. Thus, a pair of two-dimensional projection points is obtained: emission projection point and receiving projection point .like Figure 3 As shown at the bottom, these two projection points and Euclidean distance on the horizontal reference plane ( Figure 3 The solid black line connecting the two hollow dots (the line in the middle) can be calculated using the law of cosines: The result obtained after projection transformation and Defined as a "two-dimensional projection point pair". The physical meaning of this two-dimensional projection point pair is: it represents the relative positional relationship between the signal source and the observation point on a horizontal cross section perpendicular to the axis of the lamp post, ignoring the height difference between the two in the vertical direction, and focusing on analyzing the horizontal geometric orientation of the two relative to the metal reflective surface (side of the lamp post).

[0062] This embodiment uses a cylindrical coordinate system. The axis projection strategy, when dealing with the shadowing problem of metal poles, considers a lamppost as a vertically erected cylindrical structure. The normal vectors on its surface are radially distributed horizontally but uniformly distributed vertically, exhibiting vertical translation invariance. This means that the core geometric constraints determining the existence of the reflected wave path and the magnitude of the reflection angle primarily depend on the horizontal orientation of the emission and reception points relative to the cylindrical cross-section, rather than the vertical height difference. Figure 3 middle and The height difference is not the key factor determining the existence of a reflection path. Without this projection dimensionality reduction step, directly searching for reflection points in a 3D Cartesian coordinate system would lead to a complex problem of finding intersections of spatial surfaces, i.e., finding the 3D tangent point between the ray and the cylindrical surface. This computational complexity is extremely high and prone to numerical solution non-convergence. Step S1011 reduces the 3D problem to a 2D plane, essentially simplifying spatial pathfinding into planar geometric construction, thus decoupling the interference of the height variable on path search. This approach not only utilizes the symmetry of the lamppost's geometry to significantly reduce the algorithm's computational cost and is more suitable for the limited computing resources of embedded web servers, but more importantly, it extracts the most decisive horizontal component in multipath reflection—because in actual streetlight scenarios, the NEMA controller is close to the lamppost, and the horizontal occlusion effect is far greater than the vertical path loss difference. Only by projecting away the interference of the height dimension can subsequent steps accurately lock the unique specular reflection path on the 2D plane using Fermat's principle. Otherwise, even extremely small height errors could prevent the 3D search algorithm from finding an exact solution that satisfies the angle of incidence = angle of reflection, leading to the failure of the entire model.

[0063] Step S1012: On the horizontal reference plane, a physical blocking boundary is constructed with the radius of the metal lamp post. Based on Fermat's principle of shortest optical path, a unique reflection tangent point that satisfies the constraint that the incident angle equals the reflection angle is searched on the physical blocking boundary using a geometric drawing method.

[0064] After obtaining the two-dimensional projection point pair on the horizontal reference plane and and the horizontal projected Euclidean distance between them Next, the physical barrier boundary is constructed. In practice, the physical radius parameter of the metal light pole is read. , at the origin In a two-dimensional plane coordinate system centered at the center, the equation is constructed as follows The circular geometric profile is defined as a physical barrier boundary, where x and y represent the horizontal and vertical coordinates in the two-dimensional plane coordinate system, respectively. The physical meaning of this boundary is: the horizontal cross-sectional projection of the metal lamppost after height decoupling; it represents a rigid solid interface through which electromagnetic waves cannot penetrate and can only undergo surface reflection.

[0065] Based on this, a search for the reflection tangent point is performed based on Fermat's principle of shortest optical path. Fermat's principle states that when light or electromagnetic waves propagate between two points, the actual path always takes the path with the extreme value of time consumption. In a homogeneous medium, this means that the reflection path must satisfy the geometric constraint that the angle of incidence equals the angle of reflection. The specific geometric construction method and solution logic are as follows: Construct a search interval. Specifically, since the specular reflection point must geometrically lie on the cylindrical surface within the field of view of the line connecting the light source and the observation point, the emission projection point is extracted according to the polar coordinate system determined in step S1011. azimuth With the receiving projection point azimuth The area between the two is defined as the effective reflection sector. The intersection of the circular boundary of the lamp post's cross-section and this effective reflection sector is used as the search path, thus locking onto the polar angle range. On the corresponding arc segment. Among them, Indicates the launch azimuth angle With the receiving azimuth angle The smaller of the two values ​​is used as the starting boundary of the interval; The larger of the two values ​​is used as the termination boundary of the interval. This closed interval physically corresponds to the shortest circular arc path connecting the transmitting and receiving sides, eliminating invalid reflection areas away from the line of sight.

[0066] An adaptive step-size vector angle bisector iteration method is used to search along the arc segment. During this process, the projected distance is utilized. Spatially normalize the search granularity: Set the initial search step size to... In other words, the greater the projection distance, the higher the requirement for angular resolution and the smaller the required search step size. This represents the polar angle increment during the search for the reflection tangent point on the arc segment, i.e., the initial search step size. This indicates a direct proportionality. Choose any initial candidate point on the arc. Construct separate launch points Point to candidate point incident vector and candidate points Point to receiving point The outgoing vector Connect the origin With candidate points This forms a radial vector, which, according to the geometric properties of a circle, is a point. Normal vector of the tangent plane .calculate With normal The included angle is used as the angle of incidence. ,calculate With normal The included angle is used as the reflection angle. The points on the arc segment are dynamically adjusted using the bisection method or gradient descent method. Location, monitoring The difference changes until it is less than a preset tolerance threshold. .

[0067] Regarding the tolerance threshold The method for determining this is introduced. Dynamic constraints are applied. Considering the wavelength characteristics of the 2.4GHz signal, to ensure that the reflection path calculation error does not cause a drastic phase jump, Instead of setting it as a fixed constant, it is set as ,in For engineering coefficients, This refers to the electromagnetic wave wavelength corresponding to the current communication frequency band. For example, when the system operates in the 2.4GHz ISM band (such as Wi-Fi or ZigBee), the electromagnetic wave wavelength λ is approximately 0.125 meters according to the formula λ=c / f, where c is the speed of light and f is the frequency. This means that when the distance between the transmitting point and the receiving point is... At longer distances, the system automatically tightens the angle tolerance to counteract the geometric amplification effect caused by long-distance transmission. When the constraints are met... At that time, lock the current candidate point. It is defined as the unique reflection tangent point that satisfies geometric optics. . unique reflection tangent point The physical meaning is: it is a specific geometric location on the cylindrical surface of the metal lamp post. For a given transmitter and receiver location, only electromagnetic waves illuminating this point and its small neighborhood can accurately reach the receiving antenna, strictly following Snell's law of reflection. Step S1012 introduces... Adaptive precision control ensures the accuracy of the solution in long-distance deployment scenarios while avoiding overcomputation in short-distance scenarios, and is the geometric foundation for building a high-precision multipath channel model.

[0068] In step S1012, under the scenario of metal pole shadowing, the multipath effect is not a chaotic diffuse reflection, but rather dominated by strong specular reflection caused by the smooth metal surface. To accurately calculate the phase difference between the reflected wave and the direct wave—the core of subsequent step S1014—an extremely precise path length of the reflected wave must first be obtained. Since the reflecting surface is a cylindrical surface rather than a plane, the position of the reflection point is not fixed on the perpendicular bisector of the line connecting the two points, but dynamically slides across the lamppost surface as the position of the moving end changes. If a precise geometric search based on Fermat's principle is not performed in S1012, and only a rough approximation is used, such as assuming the reflection point is at the midpoint of the tangent, millimeter-level errors will occur in the calculation of the reflection path length. For example, in the 2.4 GHz band (wavelength approximately 12.5 cm), although millimeter-level path errors seem small, they are amplified in the phase domain, causing the calculated phase cancellation area to deviate from the actual physical environment; for example, a deep pit might be calculated as a wave crest. Therefore, step S1012 is the geometric foundation for establishing a high-precision multipath channel model. It ensures that the subsequently constructed virtual image and path difference have a real physical correspondence, and is a key step to achieve accurate prediction of MTU black holes rather than fuzzy guessing.

[0069] Step S1013: Construct a tangent plane at the unique reflection tangent point, and define the tangent plane as an instantaneous local reflection mirror;

[0070] Construct a virtual mirror image of the transmitting projection point in the two-dimensional projection point pair with respect to the instantaneous local reflecting mirror surface, connect the virtual mirror image point with the receiving projection point in the two-dimensional projection point pair to form an equivalent straight reflection path, and connect the transmitting projection point with the receiving projection point to form a physical direct reflection path.

[0071] like Figure 4 The diagram shown is a schematic diagram of the reflection path construction and equivalent expansion based on the virtual mirror method provided in this embodiment.

[0072] Reference Figure 4 In the diagram, O represents the origin of the center of the cross-section of the metal lamp post; (The red dot) represents the unique reflection tangent point locked using Fermat's principle; the instantaneous local reflection mirror surface (tangent) (horizontal dark gray dashed line) is the tangent plane passing through the tangent point and perpendicular to the normal; the illustration... (The gray dot in the lower left corner) and (The gray dots in the lower right corner represent the transmitting and receiving projection points, respectively; illustration) (The virtual dot in the upper left corner) is the virtual mirror image of the emission point with respect to the mirror surface; illustration (Blue dashed line) represents the equivalent straight reflection path through the lens surface; illustration (Gray dashed line) represents the physical direct path; the physical incident path and physical reflection path (red solid line) shown in the diagram represent the broken line trajectory of the electromagnetic wave's actual propagation.

[0073] In step S1013, refer to Figure 4 The only reflection tangent point on the locked horizontal reference plane Next, a virtual mirror unwrapping operation is performed to transform the polyline reflection path into a geometrically equivalent straight path. The specific implementation process is as follows: a virtual mirror point of the emission point is constructed, such as... Figure 4 As shown, with the origin Connect the origin to the center of the circle. With the unique reflection tangent point The connection ( Figure 4 The vertically downward dashed line (in the middle) represents the normal direction of the reflecting surface at the point of tangency; passing through the point... Draw a tangent perpendicular to the normal ( Figure 4 The tangent line (a dark gray dashed line in the horizontal direction) is defined as an instantaneous local reflecting mirror. The physical basis for defining this tangent line as an instantaneous local reflecting mirror is that, in the microwave frequency band involved in this embodiment (e.g., 2.4 GHz), the wavelength of electromagnetic waves is much smaller than the radius of curvature of the metal lamp post. According to the principles of differential geometry, at the reflection point... Within an infinitesimal neighborhood, the cylindrical surface can be approximated as a flat tangent plane. Therefore, for a ray at a specific incident angle, the reflection behavior of this infinitesimal region is equivalent to that of a plane mirror.

[0074] See Figure 4 Using the instantaneous local reflecting mirror as the axis of symmetry, and based on the principle of axial symmetry in plane geometry, the projection point of the emission is calculated. The point symmetrical about this axis of symmetry is defined as a virtual mirror point. ( Figure 4 (Virtual image point in the upper left corner). Virtual mirror point. geometric coordinates The following vector relationships can be used to solve the problem: ,in Represents starting from the origin Point to virtual mirror point Position vector; Represents starting from the origin Pointing to the projection point Position vector; Represents starting from the origin Pointing to the unique reflection tangent point Position vector; Represents the tangent point The unit normal vector perpendicular to the tangent plane; Represents from the launch point Point of tangency The incident direction vector; This represents the dot product operation of vectors. The physical meaning of this formula is to double the projection component of the incident vector onto the normal direction, thereby determining the position of the mirror point.

[0075] The implementation method for connecting the virtual mirror point and the receiving point to form an equivalent straight-line reflection path, and connecting the transmitting point and the receiving point to form a physical direct-path, is as follows: Two key physical propagation paths are constructed, such as... Figure 4 As shown by the blue dashed line and the blue solid line, connect the virtual mirror points. With the receiving projection point , forming a straight line segment Define the line segment as the equivalent straight reflection path. According to the principles of geometric optics, this line segment With physical barrier boundary ( Figure 4 The intersection of the circular outlines in the image coincides exactly with the unique reflection tangent point. Furthermore, based on the property of axisymmetry, the length of the equivalent straight reflection path... Numerically, it is strictly equal to the sum of the physical reflection path lengths, that is... ,in, represent Figure 4 The solid red line in the middle indicates the launch point. to the tangent point The Euclidean distance of the incident ray segment; represent Figure 4 The solid red line in the middle indicates the point of tangency. to the receiving point The Euclidean distance of the reflection line segment, This represents the Euclidean distance of the equivalent straight reflection path. For example... Figure 4 As shown by the gray dashed line, directly connect the projection point. With the receiving projection point This forms a straight line segment representing line-of-sight propagation, and this line segment is defined as the physical direct path. Its length is the horizontal projection distance calculated in step S1011. Virtual mirror point Equivalent straight reflection path The physical meaning is that they are mathematical tools that transform complex problems of polygonal reflection and propagation into simple linear propagation problems. In the physical model, the reflected wave appears to originate from a virtual source behind the mirror. The reflected wave travels directly through the lens surface to the receiving point. This geometric transformation not only visually demonstrates the total propagation distance of the reflected wave, but more importantly, it simplifies the calculation of the reflection path length to the Euclidean distance between two points, avoiding the floating-point accumulation error that may be introduced by summing the piecewise linear lengths.

[0076] In step S1013, when constructing a high-precision interference model, the phase calculation is extremely sensitive to path length. For example, in the 2.4GHz band, a path difference of only about 6cm for half a wavelength can cause the signal to change from enhancement to cancellation. The virtual mirror method used in step S1013 is the most rigorous physical method for solving the reflection path calculation. Its indispensability is reflected in two aspects: 1) Geometric equivalence ensures accuracy. Since the surface of the metal lamp post is a cylindrical surface with continuously changing curvature, rather than an ideal plane, directly measuring the broken-line distance between the emission point, reflection point, and receiving point is easily affected by small perturbations in the coordinates of the reflection point. By constructing a virtual mirror of the tangent plane, it is actually... Approximating curved surface reflection as planar reflection (tangential plane approximation) within a small neighborhood is the most accurate method for calculating optical path in the calculus sense. 2) It provides direct input for phase difference calculation; the path difference to be calculated in subsequent step S1014 is essentially... ,in This represents the Euclidean distance of the physical direct path. If not explicitly constructed via S1013... This equivalent straight line, instead of attempting to estimate the reflection path through other approximations, will fail to capture the nonlinear path growth caused by changes in the positions of the transmitting and receiving points. This is especially true when the mobile device is moving rapidly. The position also changes dynamically. Only through real-time mirror construction can this dynamic Doppler path stretching effect be captured, thereby ensuring that the final output channel fading distribution map can accurately reflect the real interference fringe positions in the physical environment.

[0077] Step S1014: Calculate the Euclidean distance difference between the equivalent straight reflection path and the physical direct path to obtain the geometric path difference;

[0078] By combining the electromagnetic wave wavelength parameters, the geometric path difference is converted into the total physical phase difference, and the set of physical phase differences is output. The total physical phase difference includes the propagation delay phase lag and the reflection phase abrupt change.

[0079] Subtracting the Euclidean length of the equivalent straight reflection path from the Euclidean length of the physical direct path yields the physical distance difference between the two during propagation in space, defined as the geometric path difference. The geometric path difference The physical meaning of this is: the physical distance that the reflected component of the electromagnetic wave energy emitted from the transmitting end at the same moment travels in space compared to the direct component that arrives directly.

[0080] The method for converting geometric path difference into physical phase difference is as follows: The electromagnetic wave frequency parameter f of the system is read. This frequency parameter depends on the communication protocol used by the IoT terminal, including but not limited to 2.4GHz-Wi-Fi, 5GHz-Wi-Fi, Sub-1GHz-LoRa, or NB-IoT. The electromagnetic wave wavelength λ is obtained according to the formula λ=c / f. Taking the most common 2.4GHz band as an example, its corresponding wavelength λ is approximately 0.125 meters. Based on the principle of linear superposition of waves, the total physical phase difference includes two physical contributions: one is the propagation delay phase lag caused by the difference in path length, and the other is the reflection phase abrupt change caused by the electromagnetic wave impacting the metal surface. According to the linear correspondence between phase and path length, the propagation delay phase lag is defined as equal to... ,in Wavenumber, representing the phase rotation per unit physical length, is compared with the geometric path difference. Multiplication converts the extra distance traveled into a lagging phase angle. Since metal lampposts are good conductors, according to electromagnetic boundary conditions, the tangential component of the electric field must be zero at the conductor surface. This forces the reflected wave to undergo a phase shift at the instant it touches the metal surface. Right now The phase flip of radians, therefore defined (Radians). Based on the principle of linear superposition of waves, the total physical phase difference is calculated using the following formula. :

[0081] ;

[0082] in, The total physical phase difference of the output is the sole quantitative basis for determining whether the two signals undergo constructive interference (signal enhancement) or destructive interference (signal fading) at the receiving end; since the mobile end may be at different positions on the horizontal reference plane (i.e., in step S1011) (These are variables). The system iterates through all discrete sampling points within the target area and performs the above calculations to generate a list containing the corresponding spatial location. The dataset is defined as the physical phase difference set.

[0083] Step S1014 is the crucial transition link between the macroscopic geometric world and the microscopic electromagnetic world. While preceding steps S1012 and S1013 precisely reconstruct the signal propagation skeleton at the macroscopic scale (meter level), the interference effects that determine the final strength of the wireless signal occur at the microscopic scale, at the centimeter or even millimeter level. Especially in shorter wavelength high-frequency bands, such as 2.4 GHz or 5 GHz, the phase is extremely sensitive to changes in path length. For example, in the 2.4 GHz band, a path difference change of only 6.25 cm (half a wavelength) can alter the phase. The radian angle causes the signal at the receiving end to flip from the moment of peak superposition (the strongest signal) to the moment of trough cancellation (the signal dead zone). Without precise calculation using the above formula in step S1014, not only can the phase change caused by distance be not quantified, but the crucial constant term in the formula will also be missed. This refers to the half-wave loss. If this term is omitted, the calculated interference fringe polarity will be completely opposite to the actual situation, treating dead zones as strong signal areas and causing the model to fail entirely. Therefore, this step is essential for bridging the gap between path search and energy field reconstruction, directly determining the physical accuracy of the final channel fading probability distribution map.

[0084] Step S102: Traverse the set of physical phase differences, mark the discrete sampling points where the total physical phase difference falls into the reverse cancellation decision interval centered on π as instantaneous signal pits, and generate pit distribution data;

[0085] To precisely define the physical range of the weakest energy numerically, this embodiment sets a value that is an odd multiple of... The central decision interval is the reverse cancellation decision interval, and the decision formula is as follows:

[0086] ;

[0087] in, Integer subscripts representing the interference series sequence, with values ​​ranging from 1 to 2. It is used to cover multiple interference troughs that appear periodically in space as the distance increases; It represents half a cycle of an electromagnetic wave, which physically corresponds to the point of phase reversal. This represents the set of all phase points where complete destructive interference can theoretically occur, i.e. ; The phase tolerance threshold representing the reverse cancellation decision is based on the receiver's minimum sensitivity threshold, such as -85dBm. It is calculated by resolving the electromagnetic wave interference energy attenuation curve to determine the maximum permissible phase deviation width when the synthesized signal strength falls below this threshold, causing communication interruption. This value is typically 0.3 to 0.5 radians. The basis for setting this decision interval is the energy synthesis law of electromagnetic wave vector superposition. Specifically, physically, the synthesized signal strength when two coherent waves (direct wave and reflected wave) converge at the receiving point is strictly controlled by the cosine function value of their phase difference. When the total physical phase difference... Evolved to near an odd multiple of - that is, satisfying At this point, the cosine function value of the phase difference approaches its negative extreme value of -1. This specific phase state is physically called out-of-phase, meaning that the electric field vector direction of the reflected wave is exactly opposite to that of the direct wave, i.e., the wave crest aligns with the wave trough. In this state, the interference effect makes the largest negative contribution to the total energy, causing the energy of the reflected wave to be significantly deducted from the energy of the direct wave. Therefore, signal strength fading does not only occur in... At this precise point, but at An energy trough area has formed nearby. The setting is precisely to quantify this physical destructive window: as long as the phase deviation is less than this threshold, the synthesized energy will drop below the communication blocking threshold due to the drastic subtraction effect. The system reads each discrete sampling point in the physical phase difference set one by one. The numerical value is then compared with the above decision model: if a certain sampling point If a point falls within the aforementioned reverse cancellation decision interval, satisfying the formula condition, then the point is determined to be in a state of reverse cancellation and is marked as an instantaneous signal pit; if a certain sampling point If a point does not fall within the specified interval, it is determined that the signal is available and is not marked. The system outputs the distribution data of frequency-selective fading pits. After traversing and screening all sampling points, the system extracts the coordinates of all discrete sampling points marked as instantaneous signal pits, constructing a discrete point set, defined as the pit distribution data.

[0088] Step S102 is a crucial decision-making step that maps abstract phase values ​​to specific communication quality states. The values ​​calculated in the preceding steps... It is merely a physical quantity and does not directly represent the quality of the signal. Step S102 introduces... and The constructed decision model establishes a logical mapping relationship between phase and signal quality. For example, in the 2.4GHz band, the wavelength is extremely short, and the phase is extremely sensitive to changes in position. This rigorous interval determination is essential to accurately filter out coordinate points that theoretically cause a sharp drop in signal strength. Without this step, the system will be unable to identify the hidden killers caused by multipath effects within the line-of-sight range, resulting in a lack of avoidance targets for subsequent one-click deployment strategies and an inability to guarantee the connection stability of IoT terminals.

[0089] Step S103: Calculate the fading hit probability of each discrete sampling point in the deep pit distribution data of multiple consecutive frames, lock the discrete sampling points with fading hit probability higher than the dynamic and static determination threshold as constant fading intervals, and generate the channel fading probability distribution map.

[0090] The deep pit distribution data reflects the instantaneous interference results under a single calculation, but in actual IoT terminal one-click deployment scenarios, the environment is highly dynamic. The presence of moving scatterers such as passing vehicles and pedestrians causes rapid changes in the multipath environment, leading to some discrete sampling points accidentally satisfying the anti-phase cancellation condition within a short period of time. If single-frame data is directly used for deployment guidance, false blind spot alarms are easily generated. Therefore, step S103 aims to distinguish between transient interference and steady-state occlusion through statistical regularities in the time dimension. The specific implementation process is as follows: the system sets a time statistical window, which includes continuously collected data... Frame deep pit distribution data, for example, taking A frame corresponds to a sampling time of approximately 1-2 seconds, where This represents the total number of frames included in the statistics. For each discrete sampling point within the target area, a corresponding fading accumulation counter is initialized in memory, with an initial value of zero. The system iterates through each frame of data within this time window in chronological order. Let the currently iterated data frame be the [number]th frame. Frame, in which , This represents the current time series index. Checking the discrete sampling point at the [current index]. Whether the point is marked as a transient signal pit in the output of step S102 of the frame. If a frame is marked (logical value 1), the counter for that frame is incremented by 1; if the frame is not marked (logical value 0), the counter remains unchanged. This process continues until all frames have been traversed. After the frame data, calculate the fading hit probability of the discrete sampling point. The calculation formula is: This probability quantifies the frequency with which a spatial location is in a signal blind zone during the observation period. A preset motion / static threshold is introduced. Recommended value to , calculate The value is compared with the threshold. When a discrete sampling point... Below the motion / static determination threshold When the signal cancels out only for a few time frames, and the signal is normal most of the time, the physical cause is that moving objects such as vehicles temporarily block or reflect the signal, creating a temporary multipath cancellation condition. As the object moves away, the interference condition is broken, and the signal recovers. This type of fading is not persistent and does not affect the long-term fixed deployment of the equipment. Based on this, the system determines that this type of discrete sampling point is occasional environmental noise and forces its fading probability to zero or ignores it in the final result. When a discrete sampling point Higher than or equal to the motion / static determination threshold When the time frame is set, it indicates that the sampling point is in a state of signal cancellation for most of the time frames. The physical cause is that the reflection source causing the phase reversal at this point is a stationary rigid object, which in this embodiment is a metal lamppost. Based on this, the system determines that such discrete sampling points belong to a constant fading interval caused by the structural occlusion of the metal lamppost and locks them in. All discrete sampling points locked into constant fading intervals are mapped onto a two-dimensional plane map, and the calculated values ​​are then used to determine the constant fading intervals. Assign the coordinates of the discrete sampling point on the map to generate a channel fading probability distribution map.

[0091] Step S103 is a crucial filter ensuring system robustness and deployment efficiency. While step S102 accurately identifies the phase cancellation point at the physical level, it cannot distinguish between moving and static areas. Without the temporal consistency check in step S103, the system will overreact to temporary signal fluctuations generated by each passing vehicle, causing frequent errors in the deployment tool or prompts for the user to move their location, severely impacting the user experience. Step S103 leverages the physical nature of structure-determined characteristics—that is, the interference fringes caused by metal lampposts have extremely high stability on the time axis, while environmental noise is transient. Through statistical analysis of fading hit probabilities, the system achieves dynamic-static separation at extremely low cost, ensuring that the final output distribution map only includes those physically insurmountable dead zones. This allows subsequent one-click deployment strategies to be targeted, avoiding only genuine physical obstacles while maintaining efficient transmission strategies in other areas.

[0092] A method for one-click deployment of software on an IoT terminal based on an embedded web server further includes the following steps:

[0093] Step S200: Using the channel fading probability distribution map and the real-time collected wireless link layer retransmission count as dual inputs, perform frame error rate coupling analysis based on Bayesian inference and extreme value optimization based on the expected function of effective throughput to solve the physical layer transmission collapse threshold, and output the application layer maximum safe load threshold after safety backoff correction.

[0094] The process of calculating the maximum safe load threshold of the application layer in step S200 specifically includes the following steps:

[0095] Step S201: Receive the channel fading probability distribution map, combine it with the current wireless link layer retransmission count, establish a nonlinear mapping relationship between packet length and frame error rate through Bayesian inference logic, and output the packet length-frame error rate coupling curve.

[0096] A binary hypothesis testing model for channel state based on Bayesian inference is constructed, specifically including the system abstracting the channel state into two mutually exclusive hypotheses: (The channel is in a high bit error rate state) and (The channel is in a low bit error rate state). The model input consists of two parts: one is the fading probability from the channel fading probability distribution map in step S103. As a prior probability reflecting the influence of the static environment, that is... Second, it comes from the retransmission count at the wireless link layer. As likelihood evidence reflecting the current instantaneous interference, the retransmission count reflects the actual delivery of data frames at the air interface at the current moment. A higher number of retransmissions indicates a worse instantaneous channel quality. The model's output is the posterior probability that the channel is indeed in a high-error-rate state. The specific calculation process is as follows: Using Bayes' theorem to fuse prior information and likelihood evidence:

[0097] ;

[0098] in, It is the likelihood function, representing the observed value under the premise of poor channel quality. The probability of a second retransmission usually follows a Poisson distribution or a binomial distribution; This is a normalization constant. Through this calculation, the system obtains a confidence value between 0 and 1, representing the true probability of the channel deteriorating at the current moment. Bayesian inference models and their formula derivation are commonly used existing techniques in the field of statistical signal processing. The specific selection of probability density functions and the calculation process of denominator normalization are well known to those skilled in the art and will not be elaborated here.

[0099] The implementation method for establishing a non-linear mapping relationship between packet length and frame error rate, and outputting the packet length-frame error rate coupling curve, is as follows: due to the posterior probability... This only represents the confidence level of state attribution, not the physical bit error rate. The system needs to convert it into a specific real-time bit error rate using a weighted logarithmic mapping method. The mapping formula is as follows: ,in, Represents the common logarithmic function with base 10. and These are the preset worst-case channel bit error rate (BER) benchmark and the optimal channel BER benchmark, respectively, as follows: and The physical meaning of this mapping process lies in using a posteriori probabilities as weighting coefficients to perform smooth interpolation between the worst and best channel models. This mapping method, which uses probability weights to estimate physical parameters, is a commonly used existing technique in adaptive algorithms for communication links and will not be elaborated upon here. In digital communication, a data packet is generated by... It consists of 10 bits. Based on the independent Bernoulli trial model, that is, assuming that the error probability of each bit is independent and cannot be recovered by error correction code, the frame error rate is... With Bao Chang and real-time bit error rate The relationship follows the nonlinear formula:

[0100] ;

[0101] The system will Substituting into the above formula, using the data packet length As the independent variable, The minimum packet length has changed from 64 bytes to a maximum transmission unit (MTU) of 1500 bytes, in order to reduce the frame error rate. Using the packet length as the dependent variable, a monotonically increasing curve is plotted, defined as the packet length-frame error rate coupling curve.

[0102] Step S201 is the core mathematical bridge for transitioning from physical layer perception to application layer decision-making. While step S103 identifies "where the pits are," i.e., their spatial distribution, it doesn't quantify "how deep these pits are," i.e., their specific destructive power to data packets of different sizes. If a fixed fragmentation strategy is used directly, sending large packets in deep pit regions will lead to… Excessive bandwidth can cause severe backoff or even connection termination in the TCP / IP protocol stack; while sending excessively small packets wastes bandwidth resources. Through step S201, the system utilizes Bayesian logic to achieve data fusion between static maps and dynamic real-time conditions. This not only corrects the blind spots of relying solely on map navigation, but more importantly, it concretizes the abstract concept of channel quality. Coupling curve. This curve provides the only mathematical basis for subsequent steps to find the throughput inflection point, enabling the system to accurately calculate the optimal data packet size that guarantees both throughput and efficiency under the current location and interference conditions. This provides a fundamental theoretical guarantee for the stability of one-click deployment.

[0103] Step S202: Construct an effective throughput expectation function based on the packet length-frame error rate coupling curve;

[0104] The effective throughput expectation function is differentiated and analyzed to find the critical inflection point where the effective throughput drops sharply, and the critical value of physical layer transmission collapse is output.

[0105] In practical communication, what truly matters is not how many bits the physical layer transmits, but rather how many valid bits the application layer successfully receives. Therefore, this embodiment defines an effective throughput expectation function based on the mathematical expectation principle in probability statistics. The function is constructed based on the following: for a length of... The data packet's single transmission has two mutually exclusive outcomes: one is successful transmission, in which case the effective benefit is... The probability of successful transmission is twofold: first, the length of the data packet itself; second, transmission failure occurs, resulting in a frame error, in which case the effective gain is 0. Based on the conclusion drawn from step S201, the probability of successful transmission is... Therefore, the statistically expected value of the effective data volume that can be obtained in a single transmission is the product of the numerical value of the gain and the probability of gain, as defined by the following formula:

[0106] ;

[0107] The communication process is modeled as a series of independent Bernoulli trials. It precisely quantifies the net payload rate that the channel can provide after deducting the time and bandwidth loss due to retransmissions caused by bit errors. When When the size is extremely small, although the success rate is close to 100%, the amount of effective data carried in a single attempt is very small. If the value is too small, the overhead of the protocol header will account for too large a proportion, resulting in an undesirable function value. Extremely low; when At its maximum, although the amount of data carried in a single operation is large, the probability of success drops exponentially, approaching zero, causing the expected function value to... It also approaches zero. Therefore, within the domain of this function, there must exist a unique maximum point, which is the highest point of system performance.

[0108] The implementation method for finding the critical inflection point where the effective throughput drops sharply by performing derivative analysis on the expected function of the effective throughput is as follows: the system uses the data packet length Using the expected effective throughput function as the independent variable, we perform a first-order derivative operation and construct a stationary point equation with a derivative of zero. The purpose of this mathematical process is to find the inflection point where the slope of the tangent line to the function curve changes from positive to negative, i.e., the equilibrium position where marginal revenue equals marginal cost. In this process, the system uses logarithmic differentiation or implicit differentiation to analytically solve the equation containing exponential terms. The differentiation rules here are common knowledge in advanced mathematics, and the derivation formulas will not be elaborated further. The optimal packet length solution that maximizes the effective throughput is then calculated. , This represents the natural logarithm function with base e; the calculated... Defined as the critical inflection point where effective throughput drops sharply. This inflection point is decisive at the physical transmission layer: when the actual length of the transmitted data packets is less than this inflection point, increasing the packet length can significantly improve throughput; however, once the data packet length exceeds this inflection point, the cost of retransmission due to the increased frame error rate will outweigh the efficiency gains brought by larger packets, leading to a precipitous drop in overall throughput. Further analysis of the impact of this critical inflection point on upper-layer protocols: Within the specified range, as packet length continues to increase, the packet loss rate... This will quickly exceed the TCP protocol's tolerance threshold. According to the congestion control mechanism of the TCP / IP protocol stack, when the receiver continuously reports packet loss, the sender may misjudge network congestion rather than physical bit errors, thus triggering the congestion avoidance algorithm, forcibly halving the sending window and entering an exponential backoff state. This cross-layer chain reaction induced by physical layer bit errors can cause the data transmission stream to momentarily stop, i.e., a transmission collapse. Therefore, the system will use the above calculations... Marked as the physical layer transmission collapse threshold, it indicates the maximum physical frame length limit that can maintain a TCP connection without congestion misjudgment under the current channel physical environment.

[0109] Step S202 prevents cross-layer protocol mismatch in the communication system. Existing IoT deployment tools often overlook the fatal impact of physical layer errors on transport layer protocols, blindly pursuing large MTUs. However, in the deep pit area caused by metal lamppost reflections, if the length of the sent data packet exceeds the critical inflection point calculated in step S202, although the physical layer is still trying to retransmit, the upper-layer TCP protocol stack will determine that the connection is broken due to timeout, causing the deployment page to fail to load. Step S202, through rigorous extreme value differentiation analysis, accurately defines a "cliff edge," ensuring that the system always stays on the safe side of the cliff (i.e., Data fragmentation is performed. This not only maximizes the utilization of channel capacity, but more importantly, it fundamentally avoids the catastrophic consequences of TCP exponential backoff induced by physical layer errors, providing an insurmountable physical red line for the subsequent security threshold correction in step S203.

[0110] Step S203: The physical layer transmission collapse threshold is adjusted downward by a preset security backoff factor, and the TCP / IP protocol header overhead is deducted to obtain the application layer maximum security load threshold.

[0111] Perform a downward adjustment based on the safety yield factor: The system introduces a preset safety yield factor. The range of values ​​for this factor is usually set as follows: to For example, take Transmitting the physical layer to the crash threshold Multiplying by this factor yields the corrected physical layer security frame length. The physical significance of introducing this factor lies in: the result obtained in step S202 Located at the peak of the effective throughput curve. In actual operating conditions, channel noise often exhibits suddenness; if the system follows the directives... Sending at maximum speed means that even a tiny noise fluctuation can cause the actual frame error rate to deviate instantly from the predicted value, resulting in a performance precipitate. Multiplying by... Making downward adjustments is equivalent to proactively taking a step back in engineering, setting the operating point in the safe zone to the left of the peak value. Although this sacrifices a negligible amount of theoretical throughput of about 10%, it gains significant link stability, preventing the system from slipping into the transmission collapse range due to calculation errors or environmental disturbances.

[0112] Determine the total overhead of the TCP / IP protocol stack header. Specifically, regarding the method for obtaining header overhead, this embodiment determines it based on the network protocol stack configuration standard currently running on the system. Specifically, the system reads the network interface layer configuration parameters of the embedded operating system to obtain the fixed lengths of the following protocol components: Ethernet frame header and trailer checksum sequence, conforming to the IEEE 802.3 standard, typically fixed at 18 bytes (14 bytes header and 4 bytes CRC checksum); IP network layer header, conforming to the RFC 791 standard, with a standard length of 20 bytes; TCP transport layer header, conforming to the RFC 793 standard, with a standard length of 20 bytes. The system adds these three together to obtain the total fixed overhead that inevitably increases when the data packet is encapsulated from the application layer down to the physical layer. .

[0113] Calculate and output the application layer maximum security payload threshold, including the modified physical layer security frame length. Subtract the above total head expenses The result is the upper limit of the effective data payload that the application layer can actually fill. To ensure that this parameter conforms to Ethernet standards, the system also compares this calculation result with the value of the maximum transmission unit of standard Ethernet minus the header overhead, and takes the smaller of the two values ​​as the final maximum secure payload threshold for the application layer. The maximum transmission unit is typically 1500 bytes. The calculation logic follows the formula: Where min represents taking the minimum value, This indicates the total overhead of the maximum transmission unit, typically 1500 bytes.

[0114] Step S203 realizes the theoretical optimization into an engineering-ready implementation, based on the calculations of the preceding steps. This is the theoretical total length of the physical waveform, including the underlying protocol header that the application cannot control. If precise subtraction in step S203 is lacking, directly... As for the fragment size, the addition of approximately 58 bytes of protocol header would cause the final physical data packet to exceed the critical value, directly sliding into the high error rate crash zone. Furthermore, by introducing a safety backoff factor, the system proactively sacrifices approximately 10%–15% of the theoretical maximum throughput in exchange for extremely high link stability under harsh operating conditions. This step ensures the final output... It is a practical parameter that conforms to the TCP / IP protocol specification and has strong anti-interference capabilities, providing a legally valid engineering input standard for the subsequent construction of accurate microframe queues for S300.

[0115] A method for one-click deployment of software on an IoT terminal based on an embedded web server further includes the following steps:

[0116] Step S300: Based on the maximum security load threshold of the application layer, perform active physical segmentation of the static resource binary stream required for the deployment page, add an auto-incrementing sequence number protocol header to the independent logical data blocks obtained by the segmentation, and generate an application layer microframe data queue.

[0117] The process of generating the application layer microframe data queue in step S300 specifically includes the following steps:

[0118] Step S301: Read the static resource binary stream to be sent from the Web server memory, and physically cut the binary stream into several independent logical data blocks with the application layer maximum security load threshold as the cutting step size;

[0119] Embedded web servers typically store web page resources in non-volatile memory as read-only arrays or file system images, such as LwIPHTTPD or GoAhead. Based on the request URL of the one-click deployment page, the starting physical address of the file to be sent is located in memory. and total file length in bytes The data at this point is a continuous, unsegmented static resource binary stream. This binary stream contains all the code logic and style information required to render the deployment page, and its bit-level integrity must be ensured during transmission. Read the application layer's maximum secure payload threshold. This is set as a fixed step size for physically segmenting the binary stream. The system bases this on the total file length. and cutting step size Calculate the total number of logical data blocks that need to be generated. The calculation formula is as follows:

[0120] ;

[0121] in This represents the floor function, which determines the total amount of data transmission and the fragmentation structure.

[0122] The implementation method for performing physical segmentation of the binary stream is as follows: To accommodate the limited memory resources of embedded devices, the physical segmentation in this embodiment does not refer to allocating new memory space for data copying, but rather employs logical pointer slicing technology. The system defines a circular index variable. By traversing , Define the offset and length of each data block in the original binary stream: for the first... The data blocks have a memory starting offset address of . Its effective data length The calculation logic is as follows: if the current block is not the last block, ,but If the current block is the last block, ,but That is, the remaining tail bytes. The system calculates based on the above. A binary tuple logically divides the original continuous binary stream into two parts. Each segment is a discrete fragment, and each segment is defined as an independent logical data block. Each independent logical data block represents a minimum transmission unit from the application layer perspective, and its size is strictly limited to the safe range that the channel can withstand.

[0123] Step S301 is the specific execution step of application-layer traffic shaping, realizing a fundamental shift from passive adaptation to proactive defense. In traditional TCP / IP communication, the application layer often disregards the underlying MTU and directly calls `send` to send large chunks of data, such as 64KB, relying entirely on the operating system's TCP protocol stack for underlying fragmentation. However, the operating system's default fragmentation is usually based on the Ethernet standard MTU, such as 1460 bytes, which falls into the "deep pit" area (physical layer transmission collapse threshold) identified in step S102. Even a small fragment (possibly only 600 bytes) is fatal—standard large TCP fragments are highly likely to be corrupted during over-the-air transmission due to excessively high error rates, leading to infinite retransmissions and connection timeouts. Step S301 enforces this at the application layer... Performing proactive physical segmentation bypasses the operating system's default behavior, ensuring that the final physical waveform length of every data packet delivered to the Socket interface, after adding the protocol header, is strictly less than [a certain value]. This eliminates the generation of excessively long data frames at the source, allowing subsequent data transmission to safely and flexibly traverse the severe fading range of the channel in the form of micro-frames.

[0124] Step S302: Add a protocol header containing an auto-incrementing sequence number to each independent logical data block, encapsulate it to obtain a microframe unit, and arrange them sequentially to generate the application layer microframe data queue;

[0125] In step S302, the encapsulation and queue construction of microframe units adopt a private protocol header dynamic splicing algorithm: traversing the set of independent logical data blocks generated in step S301, for each independent logical data block, dynamically allocating header space in memory and writing a private protocol header. The private protocol header contains only one core field: an auto-incrementing sequence number. The assignment of the auto-incrementing sequence number reuses the slice loop index variable in step S301. Alternatively, a global counter can be used. Its physical significance lies in assigning each independent logical data block a unique identity coordinate within the entire file stream. This coordinate not only identifies the order of the data blocks but also serves as a unique index key for calculating round-trip delay in subsequent steps, ensuring that the sender can identify which historical frame the acknowledgment message returned by the receiver corresponds to. The generated private protocol header is concatenated to the front of the independent logical data blocks, forming a complete transmission carrier, defined as a microframe unit. A microframe unit consists of a private protocol header and independent logical data blocks. All microframe units are then strictly pushed into a first-in-first-out (FIFO) data structure according to their auto-incrementing sequence numbers, generating an application-layer microframe data queue.

[0126] Step S302 transforms data slices into controllable transmission. Although step S301 solves the physical size problem of packets being too large to transmit, without the encapsulation in step S302, discrete data blocks are like scattered sand; the sending end cannot track their transmission status, and the receiving end cannot identify their assembly order. (Sequence number in step S302) This is the only link between step S301 (slicing) and steps S401 / S402 (flow control): in the subsequent S402, the system needs to calculate the dynamic network congestion index, which relies on accurately measuring the round-trip time of each data packet. Only through this step can each microframe be uniquely labeled. Only with tags can the sending end accurately match the ACK to the specific microframe it receives, thus calculating the accurate round-trip time delay. Therefore, step S302 not only achieves ordered data management but also provides an indispensable data foundation for subsequent channel congestion diagnosis based on accurate delay measurement.

[0127] A method for one-click deployment of software on an IoT terminal based on an embedded web server further includes the following steps:

[0128] Step S400: Based on the application layer microframe data queue, calculate the dynamic network congestion index, drive the dual-threshold segmented flow control strategy to adaptively adjust the microframe sending rhythm, and output a one-click deployment page.

[0129] The process of establishing a closed-loop flow control transmission process in step S400 specifically includes the following steps:

[0130] Step S401: Traverse the application layer microframe data queue, write the microframe into the kernel buffer and send it, and statistically analyze the transmission characteristics in real time based on the application layer confirmation message fed back by the mobile terminal, and output the dynamic network congestion index.

[0131] Traverse the application-layer microframe data queue generated in step S302. For each microframe unit in the queue, perform memory addressing and transmission operations based on the slice parameters from step S301; utilize tuples... According to the formula Precisely locate the physical start address of the current microframe in the static resource binary stream. Read from that physical starting address The payload consists of bytes of data of length S302. A non-blocking Socket send interface is invoked to assemble this payload with the private protocol header generated in step S302 and write it into the kernel send buffer of the embedded operating system. The underlying network card driver is responsible for modulating it into radio waves and sending it to the air interface. Simultaneously with the write operation, the current tick value of the hardware clock is read as the send timestamp. And this timestamp is compared with the auto-incrementing sequence number in the microframe protocol header ( A key-value pair mapping is established and stored in an in-memory microframe mapping table. This in-memory microframe mapping table is a hash table or lookup table structure used to temporarily register the status of microframes that have left the device but have not yet received a response. The key is the sequence number, and the value is the sending time. An independent receiving processing thread is started to capture the application-layer acknowledgment (ACK) message returned by the mobile terminal for each microframe in real time. The corresponding acknowledgment sequence number is parsed from the ACK message, and the current clock is immediately read as the receiving timestamp. Based on the confirmation sequence number, retrieve the corresponding transmission timestamp from the in-transit microframe mapping table, and perform interpolation to calculate the instantaneous round-trip delay. The calculation formula is: The physical meaning of this delay is: in a multipath reflection environment with a metal pole, this value mainly reflects the cumulative time cost consumed by the data frame in the transmission-failure-backoff-retransmission cycle caused by channel pits at the wireless link layer; based on the sliding window statistical algorithm, a first-in-first-out historical RTT sampling window is maintained, and the weighted moving average of the RTT samples within the window is calculated. The average value is compared with the preset link failure latency threshold. Perform normalized comparison and output dynamic network congestion index. .

[0132] In conventional network programming, step S401 involves the TCP protocol stack automatically handling all packet loss and retransmission at the lower level to provide reliable transmission, thus shielding the upper-layer application from the true quality of the link. This leads to a situation where, in scenarios where the application layer is completely shielded from the true quality of the link, the application layer is often unaware of channel degradation until the TCP connection completely times out. Step S401, by establishing an independent probe mechanism based on application-layer ACKs and strictly utilizing the slicing parameters from step S301 for precise memory addressing and transmission, effectively constructs a bypass telemetry channel on top of the TCP protocol. In this process, the in-transit microframe mapping table plays a crucial role in asynchronous state bridging: since sending and receiving are two non-blocking parallel processes, without this mapping table recording the time of transmission, the receiving thread cannot calculate "how long has passed" upon receiving the ACK, and therefore cannot obtain the true quality of the link. This step utilizes Its extremely high sensitivity to the number of retransmissions at the lower layer transforms invisible physical layer interference into visible application layer latency fluctuations. The output dynamic network congestion index not only quantifies the current channel throughput but also provides the only real-time data support for the subsequent step S402 to implement precise negative feedback flow control.

[0133] Step S402: Compare the dynamic network congestion index with the idle threshold and the congestion threshold, perform segmented flow control with full-speed transmission, linear adjustment and forced avoidance, adjust the microframe transmission interval in real time until all microframes are confirmed to be received, and output the one-click deployment page.

[0134] In step S402, the adaptive adjustment of the microframe transmission rate adopts a nonlinear negative feedback flow control algorithm based on a dual threshold interval: in the transmission loop of the application layer microframe data queue, before each extraction and transmission of the next microframe unit, the dynamic network congestion index is read. The index is compared with a preset idle threshold. For example, 0.2, and the congestion threshold. For example, a value of 0.8 is used for numerical comparison, and based on the comparison results, the current channel state is divided into three independent control intervals, implementing a segmented sleep strategy. The specific segmented adjustment logic is as follows: 1) Full-speed transmission interval: When a full-speed transmission interval is detected... When the channel is determined to be extremely clear, the system will automatically enter a sleep mode for an extended period. Locked to the preset minimum microframe transmission interval For example, 5 milliseconds, at which point the system is running at maximum throughput; 2) Forced avoidance zone: when detected When the system determines that the channel is about to collapse, it will actively sleep for a specified period of time. Locked to the preset maximum microframe transmission interval For example, 100 milliseconds. At this time, the system is forced to enter the slowest keep-alive transmission mode to prevent further deterioration due to physical layer packet loss; 3) Linear adjustment region: when detected When the channel is determined to be in a state of load fluctuation, the system performs linear interpolation calculation, using the following formula: At this point, the transmission interval increases linearly with the increase of the congestion index. The system calculates based on the above logic... The system executes the `usleep` or `vTaskDelay` function to suspend the thread. After the sleep period ends, it performs the subsequent microframe socket write operation. This process continues to loop until all microframes in the application layer microframe data queue have received the corresponding ACK confirmation message. At this point, the system determines that the static resource transmission is complete, sends an HTTP response end marker to the mobile client, and triggers the mobile browser to parse the complete HTML / JS code and render the one-click deployment page containing all form controls.

[0135] Step S402 is the last line of defense to ensure the smooth landing of data streams, implementing application-layer proactive flow control. Although S203 limits packet size and S301 performs slicing, without the time control of S402, a large number of microframes would be suddenly written to the kernel in a short period of time, causing the underlying MAC layer's sending queue to overflow instantly. Step S402 introduces dual-threshold segmented control, which has higher engineering value than simple linear adjustment: setting an idle threshold avoids the system's hypersensitivity to minor channel fluctuations, i.e., noise, ensuring deployment speed in good environments; setting a congestion threshold sets a safety red line, forcibly applying the brakes in adverse environments, ensuring that the rate of data injection into the channel is always lower than the channel's actual physical carrying capacity. This mechanism effectively avoids the false triggering of the TCP congestion algorithm, ensuring that the one-click deployment page can load slowly but steadily under adverse channel conditions.

[0136] Example 2

[0137] like Figure 2 As shown, a one-click deployment software system for IoT terminals based on an embedded web server includes:

[0138] The channel fading module is used during the initialization phase of establishing a connection between the web server and the mobile device. Based on the geometric positional relationship between the NEMA controller and the metal lamp post, it calculates the reflection interference physical field based on the solid geometric projection and outputs a channel fading probability distribution map.

[0139] The security payload module is used to take the channel fading probability distribution map and the real-time collected wireless link layer retransmission count as dual inputs, perform frame error rate coupling analysis based on Bayesian inference and extreme value optimization based on the expected function of effective throughput, calculate the physical layer transmission collapse threshold, and output the application layer maximum security payload threshold after security backoff correction.

[0140] The microframe queue module is used to perform application-layer active physical segmentation on the static resource binary stream required for the deployment page based on the application layer maximum security load threshold, add an auto-incrementing sequence number protocol header to the segmented independent logical data blocks, and generate an application layer microframe data queue.

[0141] The flow control transmission module is used to calculate the dynamic network congestion index based on the application layer microframe data queue, drive the dual-threshold segmented flow control strategy to adaptively adjust the microframe sending rhythm, and output a one-click deployment page.

[0142] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0143] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0144] The foregoing description is illustrative of the invention and should not be construed as limiting it. Although several exemplary embodiments of the invention have been described, those skilled in the art will readily understand that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the invention. Therefore, all such modifications are intended to be included within the scope of the invention as defined in the claims. It should be understood that the foregoing description is illustrative of the invention and should not be construed as limiting it to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The invention is defined by the claims and their equivalents.

Claims

1. A method for one-click deployment of software for IoT terminals based on an embedded web server, characterized in that, The method includes: During the initialization phase of establishing a connection between the web server and the mobile device, based on the geometric positional relationship of the NEMA controller relative to the metal lamp post, the physical field of reflection interference based on the solid geometric projection is calculated, and the channel fading probability distribution map is output. The channel fading probability distribution map and the real-time collected wireless link layer retransmission count are used as dual inputs to perform frame error rate coupling analysis based on Bayesian inference and extreme value optimization based on the expected function of effective throughput. The physical layer transmission collapse threshold is calculated and the maximum safe load threshold of the application layer is output after safety backoff correction. Based on the maximum security load threshold of the application layer, the application layer actively performs physical segmentation on the static resource binary stream required for the deployment page, adds an auto-incrementing sequence number protocol header to the independent logical data blocks obtained by segmentation, and generates an application layer microframe data queue. Based on the application-layer microframe data queue, the dynamic network congestion index is calculated, driving the dual-threshold segmented flow control strategy to adaptively adjust the microframe sending rhythm, and outputting a one-click deployment page.

2. The method for one-click deployment of software for an IoT terminal based on an embedded web server according to claim 1, characterized in that, The steps for creating the output channel fading probability distribution map include: Obtain the relative spatial coordinates of the mobile device and the NEMA controller, combine them with the physical size parameters of the metal light pole, perform geometric optical path construction based on cylindrical projection dimensionality reduction and virtual image unfolding, and output the physical phase difference set; Traverse the set of physical phase differences, mark the discrete sampling points where the total physical phase difference falls into the reverse cancellation decision interval centered on π as instantaneous signal pits, and generate pit distribution data.

3. The method for one-click deployment of software for an IoT terminal based on an embedded web server according to claim 2, characterized in that, The step of creating the output channel fading probability distribution map further includes: The fading hit probability of each discrete sampling point in the deep pit distribution data of multiple consecutive frames is statistically analyzed. The discrete sampling points with fading hit probabilities higher than the dynamic and static determination threshold are locked as constant fading intervals, and the channel fading probability distribution map is generated.

4. The method for one-click deployment of software for an IoT terminal based on an embedded web server according to claim 2, characterized in that, The steps for constructing the geometric optical path based on cylindrical projection dimensionality reduction and virtual image unfolding include: Establish a cylindrical coordinate system with the central axis of the metal lamp post as the Z-axis, and project the physical transmitter point of the NEMA controller and the physical receiver point of the mobile terminal onto the same horizontal reference plane along the Z-axis to obtain a two-dimensional projection point pair; On the horizontal reference plane, a physical blocking boundary is constructed with the radius of the metal lamp post. Based on Fermat's principle of shortest optical path, a unique reflection tangent point that satisfies the constraint that the incident angle equals the reflection angle is searched on the physical blocking boundary using a geometric construction method.

5. The method for one-click deployment of software for an IoT terminal based on an embedded web server according to claim 4, characterized in that, The step of constructing the geometric optical path based on cylindrical projection dimensionality reduction and virtual image unfolding also includes: A tangent plane is constructed at the unique reflection tangent point, and the tangent plane is defined as an instantaneous local reflection mirror. Construct a virtual mirror image of the transmitting projection point in the two-dimensional projection point pair with respect to the instantaneous local reflecting mirror surface, connect the virtual mirror image point with the receiving projection point in the two-dimensional projection point pair to form an equivalent straight reflection path, and connect the transmitting projection point with the receiving projection point to form a physical direct reflection path.

6. The method for one-click deployment of software for an IoT terminal based on an embedded web server according to claim 5, characterized in that, The step of constructing the geometric optical path based on cylindrical projection dimensionality reduction and virtual image unfolding also includes: The geometric path difference is obtained by calculating the Euclidean distance difference between the equivalent straight reflection path and the physical direct path; By combining the electromagnetic wave wavelength parameters, the geometric path difference is converted into the total physical phase difference, and the set of physical phase differences is output. The total physical phase difference includes the propagation delay phase lag and the reflection phase abrupt change.

7. The method for one-click deployment of software for an IoT terminal based on an embedded web server according to claim 1, characterized in that, The step of setting the maximum safe load threshold for the output application layer includes: The channel fading probability distribution map is received, and combined with the current wireless link layer retransmission count, a nonlinear mapping relationship between packet length and frame error rate is established through Bayesian inference logic, and a packet length-frame error rate coupling curve is output. Construct an effective throughput expectation function based on the packet length-frame error rate coupling curve; The effective throughput expectation function is differentiated and analyzed to find the critical inflection point where the effective throughput drops sharply, and the critical value of physical layer transmission collapse is output. The physical layer transmission collapse threshold is adjusted downward by a preset security backoff factor, and the TCP / IP protocol header overhead is deducted to obtain the application layer maximum security load threshold.

8. The method for one-click deployment of software for an IoT terminal based on an embedded web server according to claim 1, characterized in that, The steps for generating the application layer microframe data queue include: Read the static resource binary stream to be sent from the Web server memory, and physically cut the binary stream into several independent logical data blocks with the maximum security load threshold of the application layer as the cutting step size; A protocol header containing an auto-incrementing sequence number is added to each independent logical data block, and microframe units are encapsulated to generate the application layer microframe data queue.

9. The method for one-click deployment of software for an IoT terminal based on an embedded web server according to claim 1, characterized in that, The steps for outputting the one-click deployment page include: The application layer microframe data queue is traversed, microframes are written into the kernel buffer and sent, and transmission characteristics are statistically analyzed in real time based on the application layer confirmation messages fed back by the mobile terminal, and a dynamic network congestion index is output. The dynamic network congestion index is compared with the idle threshold and the congestion threshold. Segmented flow control is performed, including full-speed transmission, linear adjustment and forced avoidance. The microframe transmission interval is adjusted in real time until all microframes are confirmed to be received, and a one-click deployment page is output.

10. A one-click deployment software system for IoT terminals based on an embedded web server, characterized in that, A method for one-click deployment of IoT terminal software based on an embedded web server, as described in any one of claims 1-9, includes: The channel fading module is used during the initialization phase of establishing a connection between the web server and the mobile device. Based on the geometric positional relationship between the NEMA controller and the metal lamp post, it calculates the reflection interference physical field based on the solid geometric projection and outputs a channel fading probability distribution map. The security payload module is used to take the channel fading probability distribution map and the real-time collected wireless link layer retransmission count as dual inputs, perform frame error rate coupling analysis based on Bayesian inference and extreme value optimization based on the expected function of effective throughput, calculate the physical layer transmission collapse threshold, and output the application layer maximum security payload threshold after security backoff correction. The microframe queue module is used to perform application-layer active physical segmentation on the static resource binary stream required for the deployment page based on the application layer maximum security load threshold, add an auto-incrementing sequence number protocol header to the segmented independent logical data blocks, and generate an application layer microframe data queue. The flow control transmission module is used to calculate the dynamic network congestion index based on the application layer microframe data queue, drive the dual-threshold segmented flow control strategy to adaptively adjust the microframe sending rhythm, and output a one-click deployment page.

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