Monitoring data transmission method and system based on wireless network

Abstract

The invention belongs to the technical field of wireless communication networks, and particularly relates to a monitoring data transmission method and system based on a wireless network, and the method comprises the steps: collecting the internal force data of a steel support and the electromagnetic interference data of a channel environment; determining the initial sending probability of each wireless sensor node; constructing a historical time window sliding queue of the wireless sensor nodes, and determining an interference time sequence smoothing factor and a final sending probability; and performing access control and data transmission scheduling on the wireless sensor nodes by using the optimized p-persistent carrier monitoring multi-channel access algorithm. According to the invention, by analyzing the numerical value change characteristics of the steel support internal force data and the electromagnetic interference data, the transmission probability of the wireless sensor node is adaptively optimized, continuous strong mechanical electromagnetic interference is effectively avoided, the network anti-interference capability is enhanced, and the low-delay, safe and stable transmission of the engineering high-risk early warning monitoring data is ensured.

Description

Monitoring data transmission method and system based on wireless network Technical Field

[0001] This invention relates to the field of wireless communication network technology. More specifically, this invention relates to a monitoring data transmission method and system based on a wireless network. Background Technology

[0002] With the accelerated development of urban underground space and large-scale infrastructure construction, safety precautions at complex construction sites such as foundation pits and high-formwork structures have increasingly become a focus of public attention. In these high-risk scenarios, it is typically necessary to deploy a massive number of wireless sensor nodes to collect key deformation and stress monitoring data, such as the internal forces of steel supports, in real time. However, traditional wired monitoring solutions are difficult to wire, costly, and easily damaged by construction machinery, while conventional wireless transmission methods are often ill-suited to the complex communication environment of construction sites, prone to data loss and delays.

[0003] One existing algorithm for access control in wireless sensor networks is the p-persistent carrier sense multiple access algorithm. This algorithm can dynamically adjust the transmission probability of nodes according to the channel state, so that nodes can transmit data with adaptive probability when they detect that the channel is idle. It has the advantages of simple implementation, flexible access, and decentralization. It can effectively avoid the rigid delay caused by fixed backoff mechanisms and become a powerful tool for wireless transmission of monitoring data in construction sites.

[0004] However, existing p-persistent carrier sense multiple access algorithms, when calculating the transmission probability of each wireless sensor node, are usually highly dependent on the magnitude of electromagnetic interference detected in the channel environment at the current instant. The high-frequency operation of a large number of large construction machines in foundation pits and high formwork sites will generate strong transient electromagnetic interference. At the same time, this electromagnetic interference usually has obvious time continuity and periodic fluctuation characteristics. If the transmission probability is adjusted only based on the magnitude of transient electromagnetic interference, it will cause the transmission probability of the node to frequently fluctuate violently. This not only fails to effectively avoid continuous strong mechanical electromagnetic interference, but also increases unnecessary backoff waiting delay, which in turn causes key early warning data such as sudden stress changes in high formwork to be delayed, thus failing to guarantee the real-time and reliability of early warning of safety accidents. Summary of the Invention

[0005] To address the technical problem that existing p-persistent carrier sense multiple access algorithms are prone to probabilistic misjudgments in environments with strong continuous mechanical and electromagnetic interference, leading to delayed transmission of early warning data and making it difficult to guarantee the real-time performance and reliability of accident early warnings, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a monitoring data transmission method based on a wireless network, comprising: collecting raw data of the internal force of steel supports at the foundation pit and high formwork site, and raw data of electromagnetic interference in the channel environment; preprocessing the data to obtain steel support internal force data and electromagnetic interference data; determining the initial transmission probability of the wireless sensor node based on the numerical change of the steel support internal force data and the electromagnetic interference data value at the current sampling time; constructing a historical time window sliding queue for the wireless sensor node, and determining the interference timing smoothing factor of the wireless sensor node based on the variance of the electromagnetic interference data sequence in the sliding queue and the mean of the absolute values ​​of the differences between the electromagnetic interference data at all adjacent sampling times in the sliding queue; optimizing the initial transmission probability based on the interference timing smoothing factor to determine the final transmission probability of the wireless sensor node; statistically analyzing all wireless sensor nodes that have newly collected data in the communication time slot and buffer that need to be transmitted, determining the set of wireless sensor nodes with transmission requirements, determining whether the wireless sensor nodes with transmission requirements should apply the final transmission probability, and using an optimized p-persistent carrier sense multiple access algorithm to perform access control and coordinated data transmission scheduling for the wireless sensor nodes.

[0007] This invention emphasizes the urgency of early warning and channel availability by calculating the initial transmission probability and utilizing the changes in steel support internal force data and the magnitude of electromagnetic interference data. By constructing an interference time-series smoothing factor, it quantifies the continuous fluctuations and distribution dispersion of electromagnetic interference, enabling the identification of persistent strong mechanical interference. By optimizing the initial transmission probability in conjunction with the interference time-series smoothing factor, it adaptively corrects the transmission probability, enhancing the anti-jitter and interference avoidance capabilities of the p-persistent carrier sense-multiple access algorithm in complex construction environments. Through access control and scheduling based on the final transmission probability, it achieves collaborative transmission of wireless sensor nodes, accurately avoiding continuous mechanical electromagnetic interference and improving the real-time performance and reliability of steel support internal force monitoring data.

[0008] Preferably, the method for obtaining the initial transmission probability is as follows: calculate the absolute value of the difference between the internal force data of the steel support at the current sampling time of the wireless sensor node and the internal force data of the steel support at the previous sampling time of the current sampling time, and normalize the absolute value of the difference to obtain a first value; apply the natural exponential function to the negative of the value of the electromagnetic interference data of the wireless sensor node at the current sampling time to obtain a second value; calculate the product of the first value and the second value; normalize the product of the first value and the second value; and use the result as the initial transmission probability.

[0009] This invention constructs a normalized term for the change in internal force of steel supports and an electromagnetic interference exponential attenuation term to assess the initial transmission probability. The normalized term for the change in internal force of steel supports reflects the urgency of the warning, while the electromagnetic interference exponential attenuation term amplifies the impact of channel availability. This results in a larger initial transmission probability when the warning is urgent and the channel is clean, and a smaller initial transmission probability when the interference is strong. This provides a reliable priority basis for the subsequent optimization of the interference timing smoothing factor.

[0010] Preferably, the method for obtaining the interference timing smoothing factor is as follows: calculate the variance of all electromagnetic interference data in the historical time window sliding queue of the wireless sensor node; apply the natural exponential function to the negative of the variance of the electromagnetic interference data to obtain a third value; calculate the mean of the absolute values ​​of the differences of electromagnetic interference data at all adjacent sampling times in the historical time window sliding queue of the wireless sensor node; apply the natural exponential function to the negative of the mean of the absolute values ​​of the differences of the electromagnetic interference data to obtain a fourth value; calculate the product of the third value, the fourth value and the initial transmission probability; and use the result as the interference timing smoothing factor.

[0011] This invention achieves the evaluation of interference timing smoothing factor by constructing an exponential decay term and an initial transmission probability term, which are products of the variance of electromagnetic interference data within a historical time window sliding queue and the mean of numerical jumps at adjacent sampling times. The exponential decay term reflects the continuous fluctuations and non-stationary characteristics of electromagnetic interference, resulting in a smaller interference timing smoothing factor when there is continuous strong electromagnetic interference, and a larger interference timing smoothing factor when the channel is stable. This provides an accurate basis for optimizing the final transmission probability.

[0012] Preferably, the method for obtaining the final transmission probability is as follows: calculate the product between the interference timing smoothing factor and the initial transmission probability, and use the result as the final transmission probability.

[0013] This invention achieves the evaluation of the final transmission probability by constructing an interference timing smoothing factor and an initial transmission probability term. The product of the interference timing smoothing factor and the initial transmission probability term reflects the combined effect of the urgency of the warning and the stability of the channel. This results in a larger final transmission probability when the warning is urgent and the interference is smooth, and a smaller final transmission probability when the interference is severe, thus providing an accurate transmission probability basis for access control.

[0014] Preferably, the determination of whether to apply the final transmission probability includes: when a wireless sensor node detects that the current wireless channel is idle, each wireless sensor node with transmission needs generates a true random number in the range of zero to one by an internal hardware random number generator; in response to the true random number not being greater than the final transmission probability, the final transmission probability is applied, thereby occupying the wireless channel and encapsulating and transmitting a data packet containing high-risk deformation warning information of the steel support internal force; in response to the true random number being greater than the final transmission probability, the final transmission probability is not applied, thereby actively suspending the transmission process and delaying it to the next communication time slot before repeating the determination process.

[0015] This invention achieves access control evaluation by constructing a judgment mechanism based on the comparison between the final transmission probability and a true random number. It reflects the randomness and adaptability of probabilistic decision-making, enabling high-probability nodes to occupy the channel first, while low-probability nodes actively avoid it, thereby effectively avoiding continuous interference time slots and improving the success rate and low latency of data transmission.

[0016] Preferably, the access control and coordinated data transmission scheduling for the wireless sensor node includes: initializing and presetting the operating parameters of the p-persistent carrier sense multiple access algorithm, setting the fixed time slot length, maximum contention retransmission count, and basic backoff window base of the wireless sensor node.

[0017] Preferably, the process of collecting raw data on the internal forces of the steel supports at the foundation pit and high formwork site, as well as raw data on electromagnetic interference in the channel environment, includes: using wireless sensor devices densely deployed at key stress nodes of the foundation pit and high formwork to collect raw data on the internal forces of the steel supports, and simultaneously using the received signal strength indication module of the built-in radio frequency transceiver chip of the wireless sensor to collect raw data on electromagnetic interference in the channel environment, thereby obtaining raw data on the internal forces of the steel supports and raw data on electromagnetic interference.

[0018] Preferably, the preprocessing to obtain the steel support internal force data and electromagnetic interference data includes: performing high-precision digital conversion on the original steel support internal force data and electromagnetic interference data using an analog-to-digital converter embedded in a microprocessor to obtain the steel support internal force data and electromagnetic interference data.

[0019] Preferably, the construction of the historical time window sliding queue of the wireless sensor node includes: dynamically maintaining a window of a preset length in the memory cache of the wireless sensor node microprocessor as the historical time window sliding queue of the wireless sensor node.

[0020] Secondly, the present invention provides a monitoring data transmission system based on a wireless network, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned monitoring data transmission method based on a wireless network is implemented.

[0021] By adopting the above technical solution, the above-mentioned wireless network-based monitoring data transmission method is generated into a computer program and stored in a memory for loading and execution by a processor. This allows for the creation of a terminal device based on the memory and processor, making it convenient to use.

[0022] The beneficial effects of this invention are as follows: This invention solves the technical problem that the traditional p-persistent carrier sense-multiple access algorithm is prone to probabilistic misjudgment in the environment of strong continuous mechanical electromagnetic interference by introducing an adaptive transmission probability optimization mechanism based on the fusion of the temporal characteristics of the change in the internal force data of steel supports and electromagnetic interference data.

[0023] This invention establishes an intrinsic mapping relationship between the initial transmission probability and the urgency of early warning by analyzing the changes in the internal force data of steel supports and the electromagnetic interference values. On this basis, it further integrates the variance of electromagnetic interference data from the historical time window sliding queue with the mean of numerical jumps at adjacent sampling times to construct an interference time series smoothing factor. This tightly couples the indicators reflecting the intensity of transient interference with the indicators reflecting the continuity of time series, thereby achieving accurate identification of continuous interference at the construction site.

[0024] This invention can calculate the final transmission probability matching the current operational complexity of each wireless sensor node at each sampling moment. By dynamically adjusting the p-persistent carrier sense multiple access algorithm, it achieves accurate matching between the transmission strategy and the channel environment. For areas with urgent warnings and stable channels, priority access is given to ensure low latency; for areas with continuous interference, active avoidance is implemented to improve reliability. Ultimately, this invention improves the accuracy and anti-interference capability of monitoring data in foundation pits and high-support structures, enhances network robustness, and ensures the timely and accurate arrival of high-risk deformation warning information, providing a solid technical guarantee for engineering safety monitoring and accident prevention. Attached Figure Description

[0025] Figure 1 is a flowchart illustrating the wireless network-based monitoring data transmission method of the present invention; Figure 2 is a comparison of the transmission probabilities of the traditional p-persistent carrier sense multiple access algorithm and the optimized p-persistent carrier sense multiple access algorithm of the present invention. Detailed Implementation

[0026] The technical solutions of 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0028] This invention discloses a monitoring data transmission method based on a wireless network. Referring to Figure 1, the method includes steps S001-S005: S001: Collect raw data of the internal force of the steel support at the foundation pit and high formwork site, as well as raw data of electromagnetic interference in the channel environment, and perform preprocessing to obtain the internal force data of the steel support and the electromagnetic interference data.

[0029] Specifically, wireless sensor devices are densely deployed at key stress nodes of the foundation pit and high formwork to collect raw data on the internal forces of the steel supports. Simultaneously, the received signal strength indication module of the built-in RF transceiver chip in the wireless sensors is used to collect raw data on electromagnetic interference in the channel environment, resulting in raw data on both the internal forces of the steel supports and the electromagnetic interference. During collection, both types of data are collected synchronously within the same time period. In this embodiment, the collection frequency for both types of data is set to ten times per second. In other embodiments, the implementer can set the collection frequency according to the specific implementation situation. The raw data on the internal forces of the steel supports and the raw data on the electromagnetic interference are then subjected to high-precision digital conversion using an analog-to-digital converter embedded in the microprocessor to obtain the raw data on the internal forces of the steel supports and the electromagnetic interference data.

[0030] S002: Determine the initial transmission probability of the wireless sensor node based on the numerical change of the internal force data of the steel support and the electromagnetic interference data of the wireless sensor node at the current sampling time.

[0031] It should be noted that existing p-persistent carrier sense multiple access algorithms rely heavily on the magnitude of electromagnetic interference detected in the channel environment at the current instant when calculating the transmission probability of each wireless sensor node. This leads to the retention of critical early warning data such as sudden changes in stress in high-support structures. Based on the structural monitoring and early warning mechanism and the principle of wireless communication, the degree of sudden change in the stress state of the support structure determines the urgency of the early warning. The noise purity of the transient channel environment directly affects the probability that the internal force data of the steel support will successfully reach the receiving gateway after transmission. Therefore, this invention combines the numerical change of the internal force data of the steel support with the magnitude of the electromagnetic interference data to determine the initial transmission probability of each wireless sensor node, which is used to characterize the priority tendency of wireless sensor nodes in competing for the wireless channel.

[0032] Specifically, the initial transmission probability satisfies the expression: In the formula, At the current sampling time, This is the serial number of the wireless sensor node. For the first The initial transmission probability of each wireless sensor node at the current sampling time. For the first The internal force data of the steel support at the current sampling time of each wireless sensor node. For the first The wireless sensor nodes at the previous sampling time The collected internal force data of the steel supports, For the first At the current sampling time, each wireless sensor node... The detected electromagnetic interference data values ​​of the channel environment. It is a natural exponential function. It is the maximum-minimum normalization function.

[0033] In the formula, The larger the value, the more likely it is to be the first. The greater the likelihood that a wireless sensor node detects a sudden change in the stress state of the supporting structure at the current sampling moment, the more likely the first wireless sensor node is to detect such a change. The higher the urgency of the early warning for each wireless sensor node at the current sampling time, the more urgent the warning becomes. The higher the probability of an initial transmission by a wireless sensor node at the current sampling time. The smaller the value, the better. The less electromagnetic interference a wireless sensor node experiences at the current sampling time, the better. The greater the probability that a wireless sensor node will successfully transmit the steel support internal force data to the receiving gateway at the current sampling time, the higher the likelihood of the first wireless sensor node successfully reaching the receiving gateway. The higher the probability of an initial transmission by a wireless sensor node at the current sampling time.

[0034] S003: Construct a historical time window sliding queue for wireless sensor nodes. Based on the variance of the electromagnetic interference data sequence in the sliding queue, and combined with the mean of the absolute values ​​of the differences between electromagnetic interference data at all adjacent sampling times in the sliding queue, determine the interference time sequence smoothing factor for wireless sensor nodes.

[0035] It should be noted that, due to the high temporal continuity and drastic fluctuations of transient electromagnetic interference during the periodic heavy-load operation of large construction machinery, the internal force data of steel supports may be swallowed up by subsequent strong interference pulses as soon as it is transmitted into the air interface. According to the principle of channel interference time series characteristic analysis, the brief low electromagnetic interference under continuous strong interference environment is very likely to be a low-level artifact that does not have long-term availability. Moreover, the non-stationary oscillation characteristics of external electromagnetic interference sources will be significantly reflected in the dispersion and transient jumps of the historical time series. Therefore, this invention combines the variance of electromagnetic interference data in the historical time window sliding queue with the average jump amplitude of adjacent sampling times to determine the interference time series smoothing factor of each wireless sensor node, which is used to characterize the probability of the current channel being in a special situation of continuous interference from mechanical equipment.

[0036] Specifically, a window of a preset length is dynamically maintained in the memory cache of the wireless sensor node's microprocessor, serving as a sliding queue for the historical time window of the wireless sensor node.

[0037] Specifically, the disturbance time series smoothing factor satisfies the following expression: In the formula, At the current sampling time, This is the serial number of the wireless sensor node. For the first Interference timing smoothing factor for each wireless sensor node at the current sampling time For the first The variance of electromagnetic interference data sequences within a sliding queue of historical time windows for each wireless sensor node. For the first The mean of the absolute values ​​of the differences between electromagnetic interference data at all adjacent sampling times within the historical time window sliding queue of a wireless sensor node. For the first The initial transmission probability of each wireless sensor node at the current sampling time. It is a natural exponential function.

[0038] In the formula, The larger the value, the more likely it is to be the first. The fewer electromagnetic interferences a wireless sensor node experiences at the current sampling time, the better. The larger the interference timing smoothing factor of a wireless sensor node at the current sampling time, the greater the impact of interference. The larger the value, the more likely it is to be the first. The greater the likelihood of continuous fluctuations and instability in the electromagnetic interference data within the historical time window sliding queue of each wireless sensor node, the more likely the data will be. The smaller the interference timing smoothing factor of a wireless sensor node at the current sampling time, the better. The larger the value, the more likely it is to be the first. The greater the likelihood of continuous fluctuations and instability in the electromagnetic interference data within the historical time window sliding queue of a wireless sensor node, the higher its reliability. The smaller the interference timing smoothing factor of a wireless sensor node at the current sampling time, the better.

[0039] S004: Optimize the initial transmission probability based on the interference timing smoothing factor to determine the final transmission probability of the wireless sensor node.

[0040] It should be noted that after obtaining the interference timing smoothing factor of the wireless sensor node, the present invention will modify and optimize the initial transmission probability according to the interference timing smoothing factor to obtain the final transmission probability. Conventional methods make relatively fixed and passive decisions during channel contention. The present invention, on the other hand, uses the interference timing smoothing factor to adaptively suppress the initial transmission probability, so that the wireless sensor node can more accurately avoid special continuous mechanical interference.

[0041] Specifically, the final transmission probability satisfies the expression: In the formula, At the current sampling time, This is the serial number of the wireless sensor node. For the first The final transmission probability of each wireless sensor node at the current sampling time. For the first The initial transmission probability of each wireless sensor node at the current sampling time. For the first Interference timing smoothing factor for each wireless sensor node at the current sampling time.

[0042] In the formula, The larger the value, the more likely it is to be the first. The more urgent the early warning of the steel support internal force data monitored by the wireless sensor node at the current sampling time and the less electromagnetic interference it is subject to, the more urgent the early warning. The greater the probability that a wireless sensor node will eventually transmit at the current sampling time. The larger it is, the more likely it is to be the first The greater the probability that the channel of a wireless sensor node is in a relatively stable state at the current sampling time, the more likely the first wireless sensor node is to be in such a state. The greater the probability that a wireless sensor node will eventually transmit at the current sampling time.

[0043] S005: Statistically identify all wireless sensor nodes that have newly collected data in the communication time slot and buffer that need to be transmitted, determine the set of wireless sensor nodes with transmission requirements, decide whether to apply the final transmission probability to the wireless sensor nodes with transmission requirements, and use the optimized p-persistent carrier sense multiple access algorithm to perform access control and coordinated data transmission scheduling for the wireless sensor nodes.

[0044] Specifically, the access control and coordinated data transmission scheduling for wireless sensor nodes include: during a communication time slot, identifying all wireless sensor nodes in the buffer that have newly collected data that need to be transmitted, and determining the set of wireless sensor nodes with transmission requirements; when a wireless sensor node detects that the current wireless channel is idle, each wireless sensor node with transmission requirements generates a true random number between zero and one using its internal hardware random number generator. If the true random number is not greater than the final transmission probability, the final transmission probability is applied, and the wireless channel is occupied to encapsulate and transmit a data packet containing high-risk deformation warning information about the internal force of the steel support; if the true random number is greater than the final transmission probability, the final transmission probability is not applied, and the transmission process is actively suspended and postponed to the next communication time slot for re-performing the decision process; simultaneously, to ensure the standardized operation of the communication time slot transition and access control process. During this process, it is also necessary to initialize and preset the operating parameters of the p-persistent carrier sense multiple access algorithm, setting the fixed time slot length, maximum contention retransmission count, and basic backoff window base for the wireless sensor node. In this embodiment, the fixed time slot length of the wireless sensor node is set to 2 milliseconds, the maximum contention retransmission count is 7, and the basic backoff window base is 16. In other embodiments, implementers can set these parameters according to the actual implementation situation. For example, when severe electromagnetic interference is frequent at the engineering site and the reliability requirements for the arrival of key early warning data are stringent, the maximum contention retransmission count and the basic backoff window base can be appropriately increased to improve the data anti-collision capability and transmission success rate. When the requirements for the extreme low latency of the alarm data of the monitoring system are high and the network node deployment is relatively sparse, the fixed time slot length and the basic backoff window base can be appropriately reduced to reduce the node backoff waiting time and improve the network's data throughput efficiency and response speed.

[0045] As shown in Figure 2, this figure visually illustrates the time-series comparison of transmission probabilities between the existing p-persistent carrier sense-multiple access (PSM) algorithm and the optimized p-persistent PSM algorithm of this invention in a complex construction environment containing persistent strong mechanical and electromagnetic interference. In the figure, the horizontal axis represents the sampling time, the vertical axis represents the transmission probability, the wavy dashed line represents the existing p-persistent PSM algorithm, and the smooth solid line represents the optimized p-persistent PSM algorithm of this invention. The vertical dotted line in the middle marks the moments of abrupt changes in the internal force data of the steel support. This figure effectively verifies the core technical principle of this invention: during periods of strong interference in the middle section, the existing p-persistent PSM algorithm, due to its over-reliance on instantaneous sensing, causes drastic and ineffective probability jitter, easily leading to channel conflicts; while the optimized p-persistent PSM algorithm of this invention successfully identifies and suppresses the transmission probability in this interval, keeping the solid line stable and low to actively avoid adverse channel conditions. Meanwhile, at the moment of sudden change in internal force data, the urgency weight of the early warning instantly breaks through the suppression effect of the interference smoothing factor, causing the solid line to produce a precise increase. This strongly proves that the present invention can not only actively avoid continuous strong interference, but also give key nodes extremely high channel competition priority when a high-risk early warning occurs, taking into account both the robustness of the monitoring network against interference and the real-time transmission of early warning data.

[0046] The present invention also discloses a monitoring data transmission system based on a wireless network, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the monitoring data transmission method based on a wireless network according to the present invention.

[0047] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A monitoring data transmission method based on a wireless network, characterized in that, include: Raw data on the internal forces of steel supports at the foundation pit and high formwork site, as well as raw data on electromagnetic interference from the channel environment, are collected and preprocessed to obtain steel support internal force data and electromagnetic interference data. Based on the numerical change of the steel support internal force data and the electromagnetic interference data at the current sampling time, the initial transmission probability of the wireless sensor nodes is determined. A historical time window sliding queue for the wireless sensor nodes is constructed. Based on the variance of the electromagnetic interference data sequence within the sliding queue, combined with the mean of the absolute values ​​of the differences between electromagnetic interference data at all adjacent sampling times within the sliding queue, an interference timing smoothing factor for the wireless sensor nodes is determined. The initial transmission probability is optimized based on the interference timing smoothing factor to determine the final transmission probability of the wireless sensor nodes. All wireless sensor nodes with newly collected data in the communication time slot and buffer that need to be transmitted are counted to determine the set of wireless sensor nodes with transmission needs. It is then determined whether the wireless sensor nodes with transmission needs should apply the final transmission probability. The optimized p-persistent carrier sense multiple access algorithm is used to perform access control and coordinated data transmission scheduling for the wireless sensor nodes.

2. The monitoring data transmission method based on a wireless network according to claim 1, characterized in that, The method for obtaining the initial transmission probability is as follows: calculate the absolute value of the difference between the internal force data of the steel support at the current sampling time and the internal force data of the steel support collected at the previous sampling time, and normalize the absolute value of the difference to obtain a first value; apply the natural exponential function to the negative of the electromagnetic interference data of the wireless sensor node at the current sampling time to obtain a second value; calculate the product of the first value and the second value; normalize the product of the first value and the second value; and use the result as the initial transmission probability.

3. The monitoring data transmission method based on a wireless network according to claim 1, characterized in that, The method for obtaining the interference timing smoothing factor is as follows: calculate the variance of all electromagnetic interference data in the historical time window sliding queue of the wireless sensor node; apply the natural exponential function to the negative of the variance of the electromagnetic interference data to obtain a third value; calculate the mean of the absolute values ​​of the differences of electromagnetic interference data at all adjacent sampling times in the historical time window sliding queue of the wireless sensor node; apply the natural exponential function to the negative of the mean of the absolute values ​​of the differences of the electromagnetic interference data to obtain a fourth value; calculate the product of the third value, the fourth value and the initial transmission probability; and use the result as the interference timing smoothing factor.

4. The monitoring data transmission method based on a wireless network according to claim 1, characterized in that, The method for obtaining the final transmission probability is as follows: calculate the product between the interference timing smoothing factor and the initial transmission probability, and use the result as the final transmission probability.

5. The monitoring data transmission method based on a wireless network according to claim 1, characterized in that, The determination of whether to apply the final transmission probability includes: when a wireless sensor node detects that the current wireless channel is idle, each wireless sensor node with transmission needs generates a true random number in the range of zero to one by an internal hardware random number generator. If the true random number is not greater than the final transmission probability, the final transmission probability is applied, and the wireless channel is occupied and a data packet containing high-risk deformation warning information of the steel support internal force is encapsulated and transmitted. If the true random number is greater than the final transmission probability, the final transmission probability is not applied, and the transmission process is actively suspended and postponed to the next communication time slot before repeating the determination process.

6. The monitoring data transmission method based on a wireless network according to claim 1, characterized in that, The access control and coordinated data transmission scheduling for wireless sensor nodes includes: initializing and presetting the operating parameters of the p-persistent carrier sense multiple access algorithm, setting the fixed time slot length, maximum contention retransmission count, and basic backoff window base of the wireless sensor nodes.

7. The monitoring data transmission method based on a wireless network according to claim 1, characterized in that, The process of collecting raw data on the internal forces of steel supports at the foundation pit and high formwork site, as well as raw data on electromagnetic interference in the channel environment, includes: using wireless sensor devices densely deployed at key stress nodes of the foundation pit and high formwork to collect raw data on the internal forces of steel supports, and simultaneously using the received signal strength indication module of the built-in radio frequency transceiver chip of the wireless sensor to collect raw data on electromagnetic interference in the channel environment, thereby obtaining raw data on the internal forces of steel supports and raw data on electromagnetic interference.

8. The monitoring data transmission method based on a wireless network according to claim 1, characterized in that, The preprocessing to obtain steel support internal force data and electromagnetic interference data includes: using a microprocessor-embedded analog-to-digital converter to perform high-precision digital conversion on the original steel support internal force data and electromagnetic interference data to obtain steel support internal force data and electromagnetic interference data.

9. The monitoring data transmission method based on a wireless network according to claim 1, characterized in that, The construction of the historical time window sliding queue of the wireless sensor node includes: dynamically maintaining a window of a preset length in the memory cache of the wireless sensor node microprocessor as the historical time window sliding queue of the wireless sensor node.

10. A monitoring data transmission system based on a wireless network, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the wireless network-based monitoring data transmission method according to any one of claims 1-9.

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