Audio and video content adaptive transmission method and system

Abstract

The invention relates to the technical field of audio and video transmission, and discloses an audio and video content adaptive transmission method and system, and the method comprises the steps: creating a video enhancement unit and a battery management unit based on the audio and video data flow and battery state data of security and protection monitoring equipment; performing video signal phase modulation on a monitoring camera in the security and protection monitoring equipment based on the video enhancement unit to obtain a phase configuration matrix; performing null phase adjustment on the battery management unit based on the battery state data and the phase configuration matrix to obtain an isolation phase matrix; residual normal batteries in the security and protection monitoring equipment are determined based on the isolation phase matrix, current balance parameters and transmission power distribution parameters are generated, the limitation of a traditional wired isolation mode is overcome, differentiated communication enhancement is provided for battery units in different positions and states, battery balance control of space perception is achieved, and the safety of the security and protection monitoring equipment is improved. The problem that a traditional unified control strategy cannot adapt to battery distribution differences is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of audio and video transmission, and particularly relates to an audio and video content adaptive transmission method and system. BACKGROUND

[0002] The existing security monitoring system mainly relies on a single wireless transmission technology, which is easily affected by factors such as building shielding and multipath fading in a complex urban environment, resulting in unstable 4K high-definition video transmission quality. In particular, in areas where monitoring devices are densely deployed, the competition for spectrum resources is fierce, and the transmission delay and packet loss rate are difficult to meet the strict requirements of real-time monitoring. At the same time, the traditional system lacks effective utilization of 5G network slicing characteristics, and cannot perform differentiated resource allocation and optimization according to different business needs. The battery management system of the security monitoring device also has significant defects. The existing technology mainly uses wired methods for battery state monitoring and fault handling, which not only increases the system complexity and maintenance cost, but also lacks a rapid isolation mechanism when a battery fault occurs, which can easily lead to fault propagation and affect the stability of the entire monitoring network. SUMMARY

[0003] The present application provides an audio and video content adaptive transmission method and system, which overcomes the limitations of traditional wired isolation methods, provides differentiated communication enhancement for battery units in different positions and states, realizes spatially aware battery equalization control, and solves the problem that traditional uniform control strategies cannot adapt to battery distribution differences.

[0004] In a first aspect, the present application provides an audio and video content adaptive transmission method, which comprises: creating a video enhancement unit and a battery management unit based on the audio and video data traffic and the battery state data of the security monitoring device; performing video signal phase modulation on the monitoring cameras in the security monitoring device based on the video enhancement unit to obtain a phase configuration matrix; performing null phase adjustment on the battery management unit based on the battery state data and the phase configuration matrix to obtain an isolation phase matrix; determining the remaining normal batteries in the security monitoring device based on the isolation phase matrix and generating current equalization parameters and transmission power allocation parameters.

[0005] In combination with the first aspect, in a first implementation manner of the first aspect of the present application, the creation of the video enhancement unit and the battery management unit based on the audio and video data traffic and the battery state data of the security monitoring device comprises: monitoring the audio and video data traffic of each monitoring camera in the security monitoring device, and simultaneously acquiring the battery state data of each battery unit in the battery pack of the security monitoring device; According to the audio and video data traffic divided by the available bandwidth resources of the mobile broadband slice in the 5G network, a load rate is obtained, and the video transmission bandwidth is determined in combination with a channel quality indicator value, a path loss parameter and the load rate; Based on the battery state data and the time slot allocation strategy of the communication slice, a battery data transmission frequency is calculated; According to the video transmission bandwidth and the battery data transmission frequency, a video enhancement unit and a battery management unit are created.

[0006] In combination with the first aspect, in a second implementation manner of the first aspect of the application, the video enhancement unit and the battery management unit are created according to the video transmission bandwidth and the battery data transmission frequency, including: The video transmission bandwidth is calculated by ratio with the maximum supported bandwidth of the mobile broadband slice in the 5G network to obtain a first load value, and the battery data transmission frequency is calculated by ratio with the maximum transmission frequency of the communication slice to obtain a second load value; The state of charge ratio of the maximum deviation value of the state of charge of each battery unit in the battery pack to the average state of charge is obtained, and the state of charge ratio is taken as a battery criticality indicator; The partition parameters of the reconfigurable intelligent surface are calculated according to the first load value, the second load value and the battery criticality indicator; According to the partition parameters, the total number of reflective units of the reconfigurable intelligent surface is proportionally distributed, a first part of reflective units is configured as a video enhancement unit, and a second part of reflective units is configured as a battery management unit.

[0007] In combination with the first aspect, in a third implementation manner of the first aspect of the application, the partition parameters of the reconfigurable intelligent surface are calculated according to the first load value, the second load value and the battery criticality indicator, including: The first load value is multiplied by a first weight coefficient to obtain a first weighted value, the second load value is multiplied by a second weight coefficient to obtain a second weighted value, and the battery criticality indicator is multiplied by a third weight coefficient to obtain a third weighted value; The first weighted value, the second weighted value and the third weighted value are added to obtain a comprehensive load value, and a nonlinear transformation process is performed based on the comprehensive load value to obtain a partition ratio; The video enhancement unit number is obtained by product calculation according to the partition ratio and the total number of reflective units of the reconfigurable intelligent surface, and the battery management unit number is obtained by subtracting the video enhancement unit number from the total number of reflective units; The partition parameters are generated according to the video enhancement unit number and the battery management unit number.

[0008] In a fourth implementation form of the first aspect, the video signal phase modulation is performed on the monitoring camera in the security monitoring device based on the video enhancement unit to obtain a phase configuration matrix, including: obtaining a channel quality indicator value and a reference signal received power measurement value between the monitoring camera and the 5G base station, and simultaneously measuring a spatial position parameter of the video enhancement unit and each monitoring camera; taking the channel quality indicator value, the reference signal received power measurement value and the spatial position parameter as channel transmission parameters; selecting a video resolution transmission strategy based on the channel quality indicator value in the channel transmission parameters; inputting the channel transmission parameters and the video resolution transmission strategy as constraint conditions into a gradient descent algorithm, and iteratively calculating an optimal phase modulation angle of each reflection unit in the video enhancement unit through a signal-to-noise ratio maximization objective function; organizing the optimal phase modulation angle into a phase configuration matrix according to a spatial arrangement order of the reflection units of the video enhancement unit.

[0009] In a fifth implementation form of the first aspect, the null-phase adjustment is performed on the battery management unit based on the battery state data and the phase configuration matrix to obtain an isolation phase matrix, including: extracting a single-cell voltage value, a discharge current value, a surface temperature value, a relative state of charge value and an alternating current impedance value of each battery cell from the battery state data and constructing battery real-time data; transmitting the battery real-time data to an LSTM model deployed on the 5G base station side through NB-IoT to perform time series feature learning and fault mode identification, and obtaining a fault battery determination result of each battery cell; generating a fault battery number according to the battery cells marked as fault states in the fault battery determination result, and according to a physical installation position and a circuit connection sequence in the battery pack; performing null-phase adjustment on the battery management unit according to the fault battery number and the phase configuration matrix to obtain an isolation phase matrix.

[0010] In a sixth implementation form of the first aspect, the null-phase adjustment is performed on the battery management unit according to the fault battery number and the phase configuration matrix to obtain an isolation phase matrix, including: querying a battery pack layout database according to the fault battery number to obtain a three-dimensional spatial coordinate of the fault battery, and measuring a spatial positioning parameter between the battery management unit and the fault battery based on the three-dimensional spatial coordinate; The space positioning parameter and the 5G carrier wavelength are taken as reference parameters, and a null phase angle sequence is calculated for each reflection unit in the battery management unit; The phase configuration matrix is superimposed with the null phase angle sequence to obtain a fused phase control parameter; The fused phase control parameter is organized into a diagonal matrix structure according to the physical arrangement positions of the reflection units in the battery management unit, and a fault battery identification bit and an isolation duration marker are added to obtain an isolation phase matrix.

[0011] In a seventh implementation manner of the first aspect, the space positioning parameter and the 5G carrier wavelength are taken as reference parameters, and a null phase angle sequence is calculated for each reflection unit in the battery management unit, including: The distance value and the angle of arrival between the fault battery and each reflection unit in the battery management unit are extracted from the space positioning parameter, and the 5G carrier wavelength is taken as a reference parameter; The phase delay parameter of each reflection unit to the fault battery is calculated based on the reference parameter, and the phase delay parameter is phase-inverted to obtain a unit null phase set; The unit null phase set is composed into a null phase angle sequence according to the numbering sequence and the spatial distribution position of the reflection units in the battery management unit.

[0012] In an eighth implementation manner of the first aspect, the remaining normal batteries in the security monitoring device are determined based on the isolation phase matrix, and current equalization parameters and transmission power allocation parameters are generated, including: The remaining normal batteries that are not isolated by faults are screened out from the isolation phase matrix, and the state of charge imbalance degree of the remaining normal batteries is calculated; The remaining normal batteries are subjected to equalization current calculation based on the state of charge imbalance degree to obtain current equalization parameters; The battery management unit in the isolation phase matrix is subjected to beam weight configuration according to the state of charge level of the remaining normal batteries to obtain RIS beam enhancement weights; The transmission power allocation parameters required by the remaining normal batteries in the data reporting process are calculated based on the RIS beam enhancement weights.

[0013] In a second aspect, the present application provides an audio and video content adaptive transmission system, which comprises: A creation module is configured to create a video enhancement unit and a battery management unit based on audio and video data traffic and battery state data of a security monitoring device; A video signal phase modulation module is configured to perform video signal phase modulation on a monitoring camera in the security monitoring device based on the video enhancement unit to obtain a phase configuration matrix. A null phase adjustment module is configured to perform null phase adjustment on the battery management unit based on the battery state data and the phase configuration matrix to obtain an isolation phase matrix. A generation module is configured to determine remaining normal batteries in the security monitoring device based on the isolation phase matrix and generate current equalization parameters and transmission power allocation parameters.

[0014] In the technical solution provided by the present application, the reconfigurable intelligent surface is divided into an audio and video enhancement unit and a battery management unit, which realizes professional configuration of hardware resources, optimizes 4K video transmission performance in the audio and video enhancement unit, and processes battery state data transmission and fault isolation in the battery management unit, avoiding the resource waste problem of traditional single-function RIS. The partition control method combining the battery criticality index with the 5G network slice load is introduced, which can dynamically adjust the RIS resource allocation according to the real-time eMBB slice load, mMTC slice load and battery state, realize efficient sharing and utilization of spectrum resources, and solve the problem of insufficient flexibility of the traditional fixed allocation method. The RIS phase shift control is deeply integrated with the 5G QoS flow mechanism, and fine audio and video transmission control is realized through a three-dimensional joint optimization strategy (phase, power, modulation and coding scheme), which significantly improves the transmission quality and system energy efficiency compared with the traditional simple power control method. A light-weight LSTM fault detection model is deployed by using the 5G MEC architecture, and the wireless communication isolation of the fault battery is realized by combining the RIS null beam technology, which overcomes the limitations of the traditional wired isolation method, provides differentiated communication enhancement for battery units in different positions and states, realizes spatially-aware battery equalization control, and solves the problem that the traditional unified control strategy cannot adapt to the difference in battery distribution. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 The step schematic diagram of the audio and video content adaptive transmission method in the embodiment of the present application; Figure 2 The structure schematic diagram of the audio and video content adaptive transmission system in the embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiments of the present application provide an audio and video content adaptive transmission method and system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not have to be used to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific flow of the embodiments of the present application is described below. Please refer to Figure 1 One embodiment of the audio and video content adaptive transmission method in the embodiments of the present application includes: Step S1, creating a video enhancement unit and a battery management unit based on the audio and video data flow and the battery state data of the security monitoring device; Specifically, a joint perception mechanism is implemented at the physical layer and the network layer, that is, real-time monitoring of audio and video data traffic of all monitoring cameras in the security monitoring device is performed, including code rate and frame rate information corresponding to various video encoding formats such as 4K, 1080P or 720P, and the running state data of each battery unit in the battery pack in the current security terminal is synchronously acquired, including key electrical parameters such as single-voltage, SOC (state of charge), surface temperature, AC impedance, and discharge current, to construct a unified input feature set of cross-modal data traffic and energy supply state. The network layer bandwidth load rate index is obtained by dividing the audio and video data traffic by the real-time available bandwidth resources corresponding to the enhanced mobile broadband (eMBB) slice in the 5G network slice architecture; the current channel quality indicator CQI between the monitoring camera and the base station and the path loss parameter PL estimated based on the actual deployment environment are combined, and a joint criterion function is used to fuse and analyze the above three parameters to determine the video transmission bandwidth that each monitoring camera can obtain under the target time window, reflecting the current physical channel capacity and the bearing level of the upper layer scheduling capability for video data. At the same time, based on the battery state data and the uplink time slot allocation strategy of the mMTC type low-power communication slice in the allocated communication resources, the battery telemetry data packet reporting period allowed by the current system is evaluated, and the data acquisition and transmission frequency that can be achieved per unit time is calculated, and the transmission frequency is used to measure the time density of the battery pack health state monitoring in the transmission resources. The video transmission bandwidth and the battery data transmission frequency are used as input control parameters, and the FPGA logic unit in the reconfigurable intelligent surface (RIS) is called to complete dynamic mapping of physical resources, and the video enhancement unit for high-frequency high-definition video signal enhancement and the battery management unit for NB-IoT protocol battery telemetry uplink path reinforcement are divided on the RIS reflectarray, thereby completing the soft-defined physical resource function partitioning.

[0019] Step S2, video signal phase modulation is performed on the monitoring camera in the security monitoring device based on the video enhancement unit, and a phase configuration matrix is obtained. Specifically, the wireless transmission environment of each monitoring camera in the monitoring system is modeled and key channel transmission parameters are obtained, including the channel quality indicator (CQI) and reference signal received power (RSRP) measurement values between the monitoring camera and the 5G base station, and the spatial position parameters between each RIS reflection unit in the video enhancement unit and the monitoring camera are measured synchronously, such as three-dimensional coordinate difference, polarization angle and reflection angle, etc. A full set of channel transmission parameters including path loss, reflection path gain, relative angle and spatial geometry is constructed. The channel quality indicator, reference signal received power measurement value and spatial position parameter are taken as input variables to construct a channel transmission model, and according to the model, a hierarchical video resolution transmission strategy is formulated for different CQI levels, that is, when the CQI is higher than the preset threshold, a 4K video channel is allocated, when the CQI is moderate, a 1080P channel is matched, and when the CQI is low, it is degraded to 720P video format. The channel transmission parameters and the selected video resolution transmission strategy are taken as the optimization input of the gradient descent algorithm, and in the optimization process, the maximum signal-to-noise ratio (SINR) of the uplink of each camera is taken as the objective function, a total channel response model including the superposition of the direct path and the RIS reflection path is constructed, the partial derivative of the objective function with respect to the phase angle of each reflection unit is calculated, and the optimal phase modulation angle of each reflection unit in the video enhancement sub-surface is solved within the convergence threshold control range (for example, 10 -6 ) to form a set of phase control parameters that satisfy the optimal transmission quality condition. According to the spatial arrangement order of all reflection units in the video enhancement unit in the physical array structure, the optimal phase modulation angle is arranged in order to construct a phase configuration matrix of complex diagonal structure.

[0020] Step S3, based on the battery state data and the phase configuration matrix, the battery management unit is adjusted by null phase, and an isolation phase matrix is obtained; Specifically, in the battery management system of the security monitoring device, a battery state acquisition and timing discrimination mechanism is established, and operating parameters are extracted from each battery monomer in real time, including monomer voltage value, discharge current value, surface temperature value, relative state of charge (RSOC) value, and alternating current impedance value measured at a frequency of 1 kHz. A high-frequency updated battery real-time data vector is constructed based on the above five types of physical quantities. The battery real-time data is continuously uploaded to the lightweight long short-term memory network (LSTM) model deployed on the 5G base station side based on the NB-IoT wireless protocol within the scheduling period, and is input into the model at a sampling frequency of 100 Hz to perform timing feature learning and abnormal pattern matching. Through the trained LSTM weight, the voltage fluctuation, impedance growth trend, and temperature rise rate in the time window are comprehensively judged, and the fault state label of each battery monomer is output. When the set combination fault judgment rule (such as voltage deviation exceeding 0.5V, temperature exceeding 45℃, and impedance growth rate exceeding 50%) is met, the corresponding monomer is determined as a fault battery. According to the battery unit marked as an abnormal state in the fault battery determination result, the fault battery number is generated based on the physical layout structure and circuit connection order of the battery pack, which contains position index information and address identifier of the mapping relationship with the RIS reflection unit. According to the fault battery number combined with the phase configuration matrix generated by the video enhancement sub-surface, the corresponding reflection unit in the battery management sub-surface is retrieved, and its phase control angle is reset. By calculating the relative angle between the incident wave direction and the battery node direction, and applying the null phase calculation formula, an interference wave front of the communication signal is constructed for each identified fault node to form a deep attenuation effect in spatial superposition, achieving a signal strength reduction of more than 30dB for the node, thereby cutting off its upload link and achieving the effect of wireless communication isolation. According to the position information of all fault battery numbers and the matched phase angle, the isolation phase matrix for controlling the battery management sub-surface is constructed, which is a complex diagonal matrix, representing the directional null control command of all RIS units corresponding to the fault node.

[0021] Step S4, determining the remaining normal battery in the security monitoring device based on the isolation phase matrix and generating current equalization parameters and transmission power allocation parameters.

[0022] Specifically, the isolation phase matrix is analyzed and identified, from which the battery monomer number corresponding to the reflection unit not subjected to the null phase modulation is screened out, and the remaining normal battery set in good actual operating state in the battery management module is mapped back. Based on the current state of charge of the normal battery, the deviation degree of the overall charge distribution is calculated, the discreteness of the charge state inside the battery pack is calculated, and whether the current system is balanced is reflected. Provide target guidance for equalization control. Call the model predictive control algorithm, according to the difference between the current state of charge of each battery monomer and the reference value, the proportional adjustment and integral adjustment combination model outputs the equalization current control amount required by each normal battery to execute, ensures that the control result meets the current amplitude safety constraint and charge change smooth constraint, avoids overshoot and thermal runaway problem. At the same time, according to the current state of charge level of the normal battery, the reflection phase angle in the battery management sub-surface is reconfigured, the reflection unit corresponding to the battery with lower state of charge is subjected to stronger phase shift, and the reflection control for the battery with higher state of charge is set to be weaker. Give the low-power battery a higher signal enhancement level in the spatial channel gain, so that the low-power battery gets priority in the scheduling of wireless communication resources. Combined with the spatial beam gain level of each battery and its path loss environment, the transmission power required for each battery to report complete state information in actual NB-IoT communication is reversely calculated, and the phase enhancement level allocated to each battery node is comprehensively considered. Generate the power allocation parameter table of each node to ensure that all remaining normal batteries complete the stable upload of battery telemetry data with the minimum power consumption under the premise of meeting the communication quality standard.

[0023] In a specific embodiment, the process of performing step S1 can specifically include the following steps: Monitor the audio and video data flow of each monitoring camera in the security monitoring device, and obtain the battery state data of each battery unit in the battery pack of the security monitoring device; According to the audio and video data flow divided by the available bandwidth resources of the mobile broadband slice in the 5G network, the load rate is obtained, and the video transmission bandwidth is determined in combination with the channel quality indicator value, the path loss parameter and the load rate; Based on the battery state data and the time slot allocation strategy of the communication slice, the battery data transmission frequency is calculated; According to the video transmission bandwidth and the battery data transmission frequency, create a video enhancement unit and a battery management unit.

[0024] Specifically, the continuity state of the front-end data source in the security monitoring system is monitored, that is, the video data flow of each monitoring camera is sensed and recorded in real time during system operation, including the encoding format, frame rate, code rate, image resolution level (such as 720P, 1080P, 4K) and the cumulative data volume change in unit time, and at the same time, the 5G QoS identifier bound with the video channel is inquired in real time by combining the scheduling control module to obtain the resource mapping state of the video service unit in the current 5G network mobile broadband slice; at the same time, the multi-parameter monitoring mechanism for the internal battery system of the security device is activated, the current voltage, current, relative state of charge, impedance, surface temperature and other key battery operation indicators are collected from each battery monomer, and a multi-dimensional battery state data set is constructed, which is normalized and time-series buffered at the edge computing node to ensure the accuracy and real-time performance of battery state perception. The network load rate index is obtained by performing dynamic ratio operation on the video data flow and the schedulable bandwidth resource in the current mobile broadband slice, reflecting the instantaneous occupation degree of eMBB resources by the video service. The network load rate is fused and evaluated in combination with the channel quality indicator and the physical path loss parameter measured between the monitoring camera and the base station to comprehensively determine the video transmission bandwidth that the camera can obtain under the current network topology, interference level and resource conditions. The battery state data is combined with the mMTC type scheduling mechanism of the communication slice, based on the low-power communication channel configuration rules in the 5G network slice, and the current resource block time slot allocation is referred to, the minimum upload period and the maximum reporting frequency allowed for each battery unit are calculated, the data reporting frequency for the NB-IoT protocol is obtained, and the data reporting frequency is directly related to the timeliness of battery system state update and the robustness of monitoring system fault tolerance response. The video transmission bandwidth and the battery data transmission frequency are used as the basis for resource scheduling, and the reconfigurable intelligent surface array unit is logically mapped under the management of the FPGA controller, according to the principle of preferentially supporting high-bit-rate video transmission and low-latency battery telemetry, the reflection area used for uplink high-definition video signal enhancement is divided as the video enhancement unit, and the spatial reflection area used to improve the stability of the NB-IoT reporting link is configured as the battery management unit, realizing the functional separation and communication optimization of the space resource. In the division process, the service load of the base station side, the antenna directivity, the user density, and the spatial relative position relationship of the camera and the battery module are combined to optimize the physical division boundary of the RIS unit, so that each sub-surface obtains the maximum channel gain coverage efficiency while meeting its functional characteristics.

[0025] In a specific embodiment, the process of creating a video enhancement unit and a battery management unit according to the video transmission bandwidth and the battery data transmission frequency can specifically include the following steps: The video transmission bandwidth is compared with the maximum supported bandwidth of the mobile broadband slice in the 5G network to obtain a first load value, and the battery data transmission frequency is compared with the maximum transmission frequency of the communication slice to obtain a second load value; The state-of-charge ratio of the maximum deviation value of the state-of-charge of each battery cell in the battery pack to the average state-of-charge is obtained, and the state-of-charge ratio is taken as a battery criticality index; The partition parameters of the reconfigurable intelligent surface are calculated according to the first load value, the second load value and the battery criticality index; According to the partition parameters, the total number of reflective units of the reconfigurable intelligent surface is proportionally distributed, a first part of the reflective units is configured as a video enhancement unit, and a second part of the reflective units is configured as a battery management unit.

[0026] Specifically, the video transmission bandwidth is calculated by the maximum supported bandwidth configured for the enhanced mobile broadband (eMBB) slice in the 5G network to obtain a first load value representing the bandwidth resource occupation degree of the current video service, reflecting the pressure degree of high-definition video stream transmission on physical resources, and being a quantitative basis for evaluating whether the system needs a larger number of reflection units to support video signal enhancement. At the same time, based on the battery telemetry data reporting frequency, combined with the maximum allowed transmission frequency defined for the massive machine type communication (mMTC) service in the communication slice, a ratio operation is performed to obtain a second load value, reflecting the transmission intensity and scheduling density of the battery telemetry service in the current period, and measuring whether the system needs to strengthen the NB-IoT link to support. To identify whether there is an imbalance risk in the running state of the battery pack, the state of charge of all battery units in the current battery pack is analyzed, the difference between the maximum state of charge value and the minimum state of charge value is extracted as the SOC deviation, and the ratio between the SOC deviation value and the average value of the state of charge of the entire battery pack is calculated as an index for measuring the battery state balance, which is defined as the battery criticality index. The larger the battery criticality index, the more obvious the state difference within the battery pack, and there is a greater imbalance risk, so more space resources need to be configured for battery data link monitoring and communication enhancement. The first load value, the second load value and the battery criticality index are used as decision inputs, and the partition parameters of the reconfigurable intelligent surface are calculated through a pre-set partition decision model. The partition parameters control the division proportion of the total reflection unit resources. The embedded FPGA control logic is called, the three types of indexes are weighted and summed by using the partition algorithm with a normalized activation function, and the boundary limiting function is used to constrain the range of partition proportion change to prevent resource extreme tilt, so that the system still maintains the dual stability of communication quality and energy regulation under the condition of service burst. According to the calculated partition parameters, the total number of reflection units possessed by the entire reconfigurable intelligent surface is divided into two functional areas in proportion, wherein the first part of the reflection units is configured as a video enhancement unit for providing spatial modulation support for phase modulation, beam focusing and multi-path interference cancellation of high-definition video signals; the second part of the reflection units is configured as a battery management unit for enhanced transmission, signal isolation and energy balance related communication tasks of battery data.

[0027] In a specific embodiment, the process of calculating the partition parameters of the reconfigurable intelligent surface according to the first load value, the second load value and the battery criticality index can specifically include the following steps: The first load value is multiplied by a first weight coefficient to obtain a first weighted value, the second load value is multiplied by a second weight coefficient to obtain a second weighted value, and the battery criticality index is multiplied by a third weight coefficient to obtain a third weighted value. The first weighting value, the second weighting value and the third weighting value are added to obtain a comprehensive load value, and a nonlinear transformation process is performed based on the comprehensive load value to obtain a partition ratio; The total number of reflective units of the reconfigurable intelligent surface is multiplied by the partition ratio to obtain the number of video enhancement units, and the number of battery management units is obtained by subtracting the number of video enhancement units from the total number of reflective units; The partition parameters are generated according to the number of video enhancement units and the number of battery management units.

[0028] Specifically, the first load value is multiplied by the first weight coefficient to obtain the first weighting value, which represents the influence degree of the current video transmission task in the entire system resource scheduling weight; the second load value is multiplied by the second weight coefficient to obtain the second weighting value, which measures the proportion of the battery telemetry link in the partition decision; and the battery criticality index is multiplied by the third weight coefficient to obtain the third weighting value, which reflects the potential communication isolation and control risk caused by the imbalance of the battery pack internal state. The first weighting value, the second weighting value and the third weighting value are added to obtain a comprehensive load value integrating the multi-task communication load and the energy state perception. A preset nonlinear mapping function is used to transform the comprehensive load value, and the nonlinear function is set in the form of an activation function with smooth transition characteristics, for example, so that the output value of the partition ratio is always within a reasonable interval range, preventing excessive resource allocation to the video enhancement unit when the video load is high, and avoiding complete resource allocation to the battery management unit when the battery criticality is high, thereby maintaining the dynamic balance between high availability and high controllability of the system. The partition ratio value obtained after the nonlinear transformation process directly participates in the resource mapping calculation, and the total number of reflective units in the reconfigurable intelligent surface is multiplied by the partition ratio to calculate the number of reflective units required for the video enhancement unit. In the physical array, the number of reflective units is set as a substructure for high-definition video signal processing. The number of reflective units allocated to the battery management task is obtained by subtracting the number of reflective units allocated to the video enhancement task from the total number of reflective units, and this part of the resource is allocated to the battery data transmission enhancement, fault isolation and energy balance tasks, so that the remote control and data acquisition tasks of the battery system during operation have sufficient communication reflection capability support in the physical space. The number of video enhancement units and the number of battery management units are encapsulated to generate structured partition parameters, which include reflective unit function mapping table, physical address interval definition, sub-surface boundary coordinate data and phase control preset bitmap information, and are loaded into the RIS array controller through the high-speed FPGA control module to realize 1 millisecond-level dynamic resource reconfiguration, thereby adjusting the reflective unit function division scheme in real time under different business demand fluctuations and battery state fluctuations.

[0029] In a specific embodiment, the process of performing step S2 can specifically include the following steps: The channel quality indication value and reference signal received power measurement value between the surveillance camera and the 5G base station are obtained, and the spatial position parameters of the video enhancement unit and each surveillance camera are measured at the same time. Channel quality indication, reference signal received power measurement, and spatial location parameters are used as channel transmission parameters; Select a video resolution transmission strategy based on the channel quality indicator value in the channel transmission parameters; The channel transmission parameters and video resolution transmission strategy are used as constraints to input the gradient descent algorithm. The optimal phase modulation angle of each reflection unit in the video enhancement unit is calculated iteratively by maximizing the signal-to-noise ratio objective function. The optimal phase modulation angles are organized into a phase configuration matrix according to the spatial arrangement order of the reflection units of the video enhancement units.

[0030] Specifically, the channel quality indicator value between each monitoring camera and the 5G base station is collected in real time through the uplink feedback process defined in the 5G communication protocol stack, reflecting the modulation and coding adaptation level of the uplink channel, measuring the physical reachability and stability of the video transmission link; at the same time, the reference signal received power measurement value is obtained to evaluate the signal strength attenuation condition and identify the path loss behavior, so as to judge whether the camera is facing deep fading or shielding interference in the current period. At the same time, based on the UWB positioning module, the camera installation drawing or the three-dimensional coordinate ranging device, the spatial position parameters between the video enhancement unit and each monitoring camera are measured, including the straight-line distance, the height difference, the azimuth angle and the reflection angle and other spatial geometric information, and the spatial parameters, the channel quality indicator value and the reference power measurement value are combined to form a channel transmission parameter set. The channel quality indicator value in the channel transmission parameter is used as the basic standard for video encoding strategy decision, that is, according to the preset channel quality level and resolution mapping relationship, the corresponding video resolution transmission strategy is dynamically selected, for example, 4K video is selected when the channel quality is high, 1080P is selected when the channel quality is medium, and 720P is selected when the channel quality is poor, so that the selected resolution matches the current uplink channel capacity, thereby maximizing the clarity and continuity of the video and avoiding excessive retransmission. The channel transmission parameter and the selected video resolution transmission strategy are jointly used as constraint inputs and loaded into the phase control optimization algorithm embedded in the FPGA module. The phase control optimization algorithm adopts a gradient descent iterative solution structure, with maximizing the signal-to-noise ratio at the receiving end as the objective function. In the optimization process, by establishing a functional relationship between the reflection path channel response and the phase modulation angle, for each reflection unit in the video enhancement unit, the relative position between the reflection unit and the target camera and the influence of the reflection unit on the signal reflection angle are considered, the phase control angle of the reflection unit is iteratively adjusted, and the increment of the overall signal enhancement contribution formed by the reflection path is evaluated in each iteration. By adjusting the directional negative gradient of the partial derivative result of the objective function, the optimal reflection state configuration is continuously approached. When the iteration error is lower than the convergence threshold, the gradient update process is interrupted, and the phase modulation angle corresponding to each reflection unit at this time is the optimal control solution under the current channel state and video resolution level. The optimal phase modulation angles of all reflection units are sequentially organized according to the physical spatial arrangement order of the reflection units in the video enhancement sub-surface to construct a video enhancement phase configuration matrix. The matrix is a diagonal structure, and each diagonal element represents the phase modulation value of the corresponding reflection unit.

[0031] In a specific embodiment, the process of performing step S3 can specifically include the following steps: extracting the single cell voltage value, the discharge current value, the surface temperature value, the relative state of charge value and the alternating current impedance value of each battery unit from the battery state data and constructing the battery real-time data; The real-time data of the battery is transmitted to the LSTM model deployed on the side of the 5G base station through the NB-IoT for time sequence feature learning and fault mode identification, and the fault battery determination result of each battery unit is obtained; According to the battery unit marked as a fault state in the fault battery determination result, a fault battery number is generated according to the physical installation position and the circuit connection sequence number in the battery pack; According to the fault battery number and the phase configuration matrix, the battery management unit is adjusted in a notch phase, and an isolated phase matrix is obtained.

[0032] Specifically, through the multi-channel acquisition module embedded in the battery management system of the security monitoring device, the key operating parameters of each battery unit are continuously collected, including single battery voltage value, discharge current value, surface temperature value, relative state of charge value and alternating current impedance value, and the five types of indicators are uniformly coded in the time dimension to form standardized battery real-time data stream. The voltage value reflects the instantaneous performance of the battery electrochemical state, the discharge current reflects the actual load response behavior, the surface temperature value reveals the battery heating and heat diffusion condition, the relative state of charge value measures the proportion of energy remaining, and the alternating current impedance value reflects the material aging and interface reaction characteristics. The above data is updated every hundred milliseconds and aggregated into a multi-dimensional time sequence feature sequence in the local buffer area, reported in low power consumption through the NB-IoT protocol in the 5G network mMTC communication slice, and sent in real time to the LSTM model deployed on the edge computing node side of the 5G base station. The LSTM model structure learns and solidifies the weights for typical battery fault modes in the pre-training stage, and its structure includes an input layer, multiple hidden state units with time memory gate control, and an output layer, where the time series input dimension corresponds to the five types of battery features. The LSTM model performs time series feature extraction and dynamic state modeling on the input battery real-time data, identifies typical fault trends such as voltage abnormal fluctuation, current overshoot, temperature rise asymmetry, SOC drift and impedance mutation, and then analyzes the degree of deviation between the current state sequence and the historical samples through the gating mechanism inside the model. When the current data trend of a battery unit matches the characteristics of a high-risk fault mode, the model determines that the battery unit is in a fault state, and outputs a judgment result marked as abnormal. The battery unit with a fault probability score higher than the set threshold is determined as a fault battery that cannot continue to participate in operation scheduling. According to the battery unit index marked as a fault state in the fault battery judgment result, combined with the physical structure mapping diagram and circuit connection topology information inside the battery pack, the fault battery number with spatial recognition is generated according to the installation level, position coordinates and parallel-serial connection order. The fault battery number is matched with the current configured video enhancement phase matrix, and the reflection unit responsible for battery uplink modulation in the battery management sub-surface is reconfigured. Based on the specific physical location of the fault battery in the spatial structure and its relative geometric relationship with the intelligent reflecting surface, the reflection phase shift angle on the battery receiving path is derived through angle calculation and array control algorithm, and a directional null is constructed through phased array modeling, so that the signal strength on the battery receiving path presents serious attenuation after reflection superposition, thereby realizing the communication interruption of the target battery. The phase adjustment strategy resets the phase of the reflection unit associated with the fault battery number in the original phase configuration matrix to the null control angle, and the remaining unaffected units maintain the original configuration. According to the boundary division structure of the video enhancement sub-surface and the battery management sub-surface, all reflection units subjected to null phase are rearranged in spatial order to form an updated isolation phase matrix, and the isolation phase matrix is loaded to the RIS controller.

[0033] In a specific embodiment, the process of performing step according to the fault battery number and the phase configuration matrix to carry out the null phase adjustment of the battery management unit to obtain the isolation phase matrix can specifically include the following steps: According to the fault battery number, query the battery pack layout database to obtain the three-dimensional space coordinates of the fault battery, and measure the spatial positioning parameters between the battery management unit and the fault battery based on the three-dimensional space coordinates; Taking the spatial positioning parameters and the 5G carrier wavelength as the reference parameters, calculate the null phase angle sequence of each reflection unit in the battery management unit; Superimpose the phase configuration matrix and the null phase angle sequence to obtain the fused phase control parameters; According to the physical arrangement position of each reflection unit in the battery management unit, organize the fused phase control parameters into a diagonal matrix structure, and add a fault battery identification bit and an isolation duration marker to obtain the isolation phase matrix.

[0034] Specifically, according to the fault battery number, a query operation is initiated in the battery number in the pre-constructed battery pack layout database. The battery pack layout database stores three-dimensional coordinate information of all battery monomers in the physical device, including the absolute installation position in the X, Y, Z three directions, the offset amount from the equipment reference datum point, and the index mapping relationship in the space grid. Through the query result, the three-dimensional coordinate point of the current battery unit marked as fault in the physical space is obtained. The three-dimensional coordinates are matched with the spatial installation coordinates of each reflection unit in the battery management unit, and the spatial positioning parameters between each pair of reflection units and the fault battery are calculated by a spatial positioning algorithm, including the Euclidean distance, the relative height difference, the horizontal deflection angle, and the incident reflection direction angle. Combined with the signal wavelength corresponding to the carrier frequency used in the 5G network, the spatial parameters are mapped to the target angle input required for wavefront phase modulation, and the zero-trap condition of the reflection signal in the path propagation is formed with the target interference point (i.e. the fault battery position) to form a wave peak cancellation. Based on this target, the corresponding zero-trap phase angle sequence of all reflection units in the battery management unit is calculated, so that the signal of each reflection path at the fault battery spatial point is superimposed to form a strong destructive interference, so that the signal strength of the target point is attenuated, thereby realizing the physical basis of spatial domain communication isolation. The zero-trap phase angle sequence is superimposed with the original phase configuration matrix to form a fused phase control parameter set. The superposition operation is a phase offset compensation for the control angle of each reflection unit under the original phase modulation strategy, so as to realize the vector level coupling optimization between the original enhanced channel and the newly added isolation channel, that is, to enhance the signal shielding ability of the fault node while not interfering with the non-fault target communication channel. According to the physical arrangement order of each reflection unit in the battery management unit, the fused phase control parameters are reorganized into a standard diagonal matrix structure, each diagonal element of the matrix corresponds to a complex phase control value of a physical reflection unit, and the matrix structure ensures the address consistency and execution order when the phase array modulation is loaded in the control hardware. In order to enhance the timeliness and identifiability of control, additional mark fields are embedded in the matrix structure, including a unique number bit for identifying the fault battery and a time tag bit representing the isolation duration, and the time tag sets whether the system maintains the zero-trap configuration state in multiple periods. When expired, the fault state needs to be re-evaluated and the phase configuration needs to be reconstructed. Finally, the isolation phase matrix integrating spatial recognition, phase fusion and strategy control is generated and sent to the RIS execution unit through the FPGA high-speed control interface.

[0035] In a specific embodiment, the execution step of calculating the zero-trap phase angle sequence of each reflection unit in the battery management unit based on the spatial positioning parameters and the 5G carrier wavelength as reference parameters can specifically include the following steps: extract the distance value and the angle of arrival between the fault battery and each reflection unit of the battery management unit from the spatial positioning parameters, and combine the 5G carrier wavelength as a reference parameter; calculate the phase delay parameter of each reflection unit to the fault battery based on the reference parameter, and perform phase inversion on the phase delay parameter to obtain a set of unit null phase; According to the numbering order and spatial distribution position of the reflection units in the battery management unit, the set of unit null phase is composed into a null phase angle sequence.

[0036] Specifically, the distance value and the angle of arrival between each fault battery monomer and all reflection units in the battery management unit are extracted from the spatial positioning parameters, wherein the distance value is used to describe the reflection path length difference, and the angle of incidence is used to represent the wave surface offset direction between the signal and the reflection surface. The signal wavelength value is obtained by combining the actual carrier frequency band of the current 5G network, and the signal wavelength is used as the reference parameter for the entire phase modulation calculation. Based on the reference parameter, a relative phase difference model is established for the reflection units to the fault battery in the path propagation process. By projecting the distance between each reflection unit and the target battery to the effective propagation path length in the incident direction, and normalizing and converting by combining the 5G carrier wavelength, the phase delay offset value of the reflected signal in the propagation process relative to the target point is derived. The phase delay parameter describes the state of the signal superimposed and interfered or enhanced at the target point, so the phase inversion operation is performed to convert the phase configuration required for "anti-interference", that is, the phase inversion processing is performed, so that each reflection unit releases a signal wave front that is out of phase with the main reflection path direction in the fault battery direction, thereby forming an effective wave interference attenuation phenomenon at the target position. Each phase angle after inversion processing becomes the target phase angle value that the reflection unit should adopt under the null strategy, and all processed angle data is aggregated into a set of unit null phases to form the basic control information of the intelligent reflecting surface for fault node shielding on the battery management sub-surface. According to the numbering order and actual spatial distribution position of each reflection unit in the battery management sub-surface, each angle value in the set of unit null phases is matched to the corresponding number in turn to form a null phase angle sequence with spatial consistency. In the process of organizing the null phase angle sequence, the arrangement density, row and column address, channel index and other details of the reflection units in the physical layout on the two-dimensional plane or three-dimensional structure are considered to ensure that each angle control value in the sequence does not occur spatial dislocation when the matrix is mapped to the actual reflecting surface. The constructed null phase angle sequence corresponds to the position number of each reflection unit, and completes the phase configuration consistently with the spatial propagation path direction, so that all reflection paths realize the most destructive interference superposition at the fault battery position.

[0037] In a specific embodiment, the process of performing step S4 can specifically include the following steps: Filtering out the remaining normal battery which is not isolated by the fault from the isolation phase matrix, and calculating the state of charge imbalance degree of the remaining normal battery; Based on the state of charge imbalance degree, the equalization current of the remaining normal battery is calculated to obtain the current equalization parameter; According to the state of charge level of the remaining normal battery, the beam weight configuration of the battery management unit in the isolation phase matrix is performed to obtain the RIS beam enhancement weight; Based on the RIS beam enhancement weight, the transmission power allocation parameter required by the remaining normal battery in the data reporting process is calculated.

[0038] Specifically, the current activated isolation phase matrix is parsed, the fault battery identification field embedded in the matrix is matched with the corresponding reflection unit space mapping table, a set of reflection units not subjected to null phase modulation is screened out, and the set is reversely mapped to a set of battery numbers to identify normal battery nodes in the current system that are still in a usable state and not isolated. After the screening is completed, based on the SOC data collected by the real-time monitoring module, the relative state of charge values are extracted from the remaining normal batteries, a statistical model based on the current charge distribution is constructed, the state dispersion degree between the battery monomers is calculated, the state of charge imbalance degree is obtained, the balance level of the internal energy distribution of the battery pack in the non-fault state is quantified, and if the value is high, it indicates that energy balancing operation needs to be performed immediately to prevent secondary abnormalities. Based on the imbalance degree calculation result, through the embedded energy scheduling control module, the model predictive control algorithm is activated to calculate the equalization current target value of each remaining normal battery monomer. The calculation process is based on the deviation between the current state of charge of each battery monomer and the overall reference state of charge, combined with the set proportional regulation coefficient and integral control boundary, and outputs the target equalization current that each unit should receive or release in the next scheduling period. In this process, the battery temperature state, current path limitation and communication scheduling window time constraint are considered to ensure that the calculated current equalization parameters are effective in value and have actual executable control conditions. The obtained current equalization parameter set is encapsulated into the battery management signaling structure as part of the control instruction. According to the SOC level of each normal battery, the reflection unit of the corresponding battery management unit in the isolation phase matrix is configured with individualized beam weight. According to the space perception enhancement mechanism, higher reflection gain weight is allocated to the battery with lower SOC value, the phase control angle of the reflection unit is adjusted to form a focusing beam in the direction of the target battery, the signal reception strength is enhanced, and the uplink success rate of the communication link is improved; for the battery with higher SOC value, the allocated RIS reflection resource is reduced to balance the overall channel load and avoid resource waste. Through the space perception enhancement mechanism, a dynamically adjustable RIS beam enhancement weight structure is constructed, and the RIS beam enhancement weight structure is dynamically updated according to the battery state of charge in each control period. According to the RIS beam enhancement weight, the current physical path loss condition and the target upload frequency of each battery unit, the transmission power is reversely deduced, the minimum effective transmission power of the target channel under the set reporting success rate requirement is estimated, and the transmission power allocation parameters required by each battery in the data reporting process are calculated.

[0039] The above describes the audio and video content adaptive transmission method in the embodiments of the present application, and the following describes the audio and video content adaptive transmission system in the embodiments of the present application. Please refer to Figure 2 An embodiment of the audio and video content adaptive transmission system in the embodiments of the present application includes: A creating module is configured to create a video enhancement unit and a battery management unit based on audio and video data traffic and battery state data of the security monitoring device; A video signal phase modulation module is configured to perform video signal phase modulation on a monitoring camera in the security monitoring device based on the video enhancement unit to obtain a phase configuration matrix; A null phase adjustment module is configured to perform null phase adjustment on the battery management unit based on the battery state data and the phase configuration matrix to obtain an isolation phase matrix; A generating module is configured to determine a remaining normal battery in the security monitoring device based on the isolation phase matrix and generate current equalization parameters and transmission power allocation parameters.

[0040] Through the cooperation of the above components, by dividing the reconfigurable intelligent surface into two functional areas of the audio and video enhancement unit and the battery management unit, the professional configuration of hardware resources is realized, the audio and video enhancement unit is specially optimized for 4K video transmission performance, and the battery management unit is specially used for processing battery state data transmission and fault isolation, avoiding the resource waste problem of traditional single-function RIS. The partition control method combining the battery criticality index with the 5G network slice load is introduced, which can dynamically adjust the RIS resource allocation according to the real-time eMBB slice load, mMTC slice load and battery state, realize the efficient sharing and utilization of spectrum resources, and solve the problem of insufficient flexibility of the traditional fixed allocation method. The RIS phase shift control is deeply integrated with the 5G QoS flow mechanism, and through the three-dimensional joint optimization strategy (phase, power, modulation and coding scheme), fine audio and video transmission control is realized, which significantly improves the transmission quality and system energy efficiency compared with the traditional simple power control method. A light-weight LSTM fault detection model is deployed by using the 5G MEC architecture, and the wireless communication isolation of the fault battery is realized by combining the RIS null beam technology, which overcomes the limitations of the traditional wired isolation method, provides differentiated communication enhancement for battery units in different positions and states, realizes the spatial perception of battery equalization control, and solves the problem that the traditional unified control strategy cannot adapt to the difference in battery distribution.

[0041] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, the system and the unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0042] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0043] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the same; even though the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some of the technical features; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for adaptive transmission of audiovisual content, characterized in that, The method comprises the steps of: creating a video enhancement unit and a battery management unit based on the audio and video data flow and the battery state data of the security monitoring device; modulating the video signal phase of the monitoring camera in the security monitoring device based on the video enhancement unit to obtain a phase configuration matrix; adjusting the null phase of the battery management unit based on the battery state data and the phase configuration matrix to obtain an isolation phase matrix; determining the remaining normal battery in the security monitoring device based on the isolation phase matrix and generating current equalization parameters and transmission power allocation parameters.

2. The adaptive transmission method of audio-visual content according to claim 1, characterized in that, The method of creating a video enhancement unit and a battery management unit based on the audio and video data flow and the battery state data of the security monitoring device comprises the steps of: monitoring the audio and video data flow of each monitoring camera in the security monitoring device, and simultaneously obtaining the battery state data of each battery unit in the battery pack of the security monitoring device; determining the video transmission bandwidth based on the audio and video data flow divided by the available bandwidth resources of the mobile broadband slice in the 5G network, and combining the channel quality indicator value, the path loss parameter and the load rate; calculating the battery data transmission frequency based on the battery state data and the time slot allocation strategy of the communication slice; creating a video enhancement unit and a battery management unit based on the video transmission bandwidth and the battery data transmission frequency.

3. The adaptive audio / video content transmission method of claim 2, wherein, The method of creating a video enhancement unit and a battery management unit based on the video transmission bandwidth and the battery data transmission frequency comprises the steps of: calculating the first load value by ratio of the video transmission bandwidth to the maximum supported bandwidth of the mobile broadband slice in the 5G network, and calculating the second load value by ratio of the battery data transmission frequency to the maximum transmission frequency of the communication slice; obtaining the state of charge ratio of the maximum deviation value of the state of charge of each battery unit in the battery pack to the average state of charge, and taking the state of charge ratio as the battery criticality index; calculating the partition parameter of the reconfigurable intelligent surface based on the first load value, the second load value and the battery criticality index; allocating the total number of reflective units of the reconfigurable intelligent surface in proportion according to the partition parameter, configuring a first part of reflective units as a video enhancement unit, and configuring a second part of reflective units as a battery management unit.

4. The adaptive audio / video content transmission method of claim 3, wherein, The method of calculating the partition parameter of the reconfigurable intelligent surface based on the first load value, the second load value and the battery criticality index comprises the steps of: multiplying the first load value by a first weight coefficient to obtain a first weighted value, multiplying the second load value by a second weight coefficient to obtain a second weighted value, and multiplying the battery criticality index by a third weight coefficient to obtain a third weighted value; performing addition operation on the first weighted value, the second weighted value and the third weighted value to obtain a comprehensive load value, and performing nonlinear transformation processing based on the comprehensive load value to obtain a partition ratio; performing product calculation based on the partition ratio and the total number of reflective units of the reconfigurable intelligent surface to obtain the number of video enhancement units, and subtracting the number of video enhancement units from the total number of reflective units to obtain the number of battery management units; Generate a partition parameter according to the number of video enhancement units and the number of battery management units.

5. The adaptive transmission method of audio-visual content according to claim 1, characterized in that, The video signal phase modulation of the monitoring camera in the security monitoring device based on the video enhancement unit obtains a phase configuration matrix, including: Obtain the channel quality indicator value and the reference signal received power measurement value between the monitoring camera and the 5G base station, and measure the spatial position parameters of the video enhancement unit and each monitoring camera; The channel quality indicator value, the reference signal received power measurement value and the spatial position parameter are used as channel transmission parameters; Select a video resolution transmission strategy based on the channel quality indicator value in the channel transmission parameter; Input the channel transmission parameter and the video resolution transmission strategy as a constraint condition into a gradient descent algorithm, and iteratively calculate the optimal phase modulation angle of each reflection unit in the video enhancement unit through a signal-to-noise ratio maximization objective function; According to the spatial arrangement order of the reflection units of the video enhancement unit, the optimal phase modulation angle is organized into a phase configuration matrix.

6. The adaptive transmission method of audio-visual content according to claim 1, characterized in that, The null phase adjustment of the battery management unit based on the battery state data and the phase configuration matrix obtains an isolation phase matrix, including: Extract the single cell voltage value, discharge current value, surface temperature value, relative state of charge value and alternating current impedance value of each battery unit from the battery state data and construct a battery real-time data; The battery real-time data is transmitted to the LSTM model deployed on the 5G base station side through NB-IoT for time series feature learning and fault mode identification, and the fault battery determination result of each battery unit is obtained; According to the battery unit marked as a fault state in the fault battery determination result, generate a fault battery number according to the physical installation position and circuit connection serial number in the battery pack; According to the fault battery number and the phase configuration matrix, the null phase adjustment of the battery management unit obtains an isolation phase matrix.

7. The adaptive audio / video content transmission method of claim 6, wherein, The null phase adjustment of the battery management unit based on the fault battery number and the phase configuration matrix obtains an isolation phase matrix, including: According to the fault battery number, query the battery pack layout database to obtain the three-dimensional spatial coordinates of the fault battery, and measure the spatial positioning parameters between the battery management unit and the fault battery based on the three-dimensional spatial coordinates; The spatial positioning parameters and the 5G carrier wavelength are used as reference parameters to calculate the null phase angle sequence of each reflection unit in the battery management unit; Superimpose the phase configuration matrix and the null phase angle sequence to obtain the fused phase control parameter; According to the physical arrangement position of each reflection unit in the battery management unit, the fused phase control parameter is organized into a diagonal matrix structure, and a fault battery identification bit and an isolation duration marker are added to obtain an isolation phase matrix.

8. The adaptive audio / video content transmission method of claim 7, wherein, The spatial positioning parameters and the 5G carrier wavelength are used as reference parameters to calculate the null phase angle sequence of each reflection unit in the battery management unit, including: Extract the distance value and angle of arrival between the faulty battery and each reflection unit of the battery management unit from the spatial positioning parameters, and combine the 5G carrier wavelength as a reference parameter; Calculate the phase delay parameter of each reflection unit to the faulty battery based on the reference parameter, and perform phase inversion on the phase delay parameter to obtain a set of unit null phase; According to the number sequence and spatial distribution position of the reflection units in the battery management unit, the set of unit null phase is composed into a sequence of null phase angles.

9. The adaptive transmission method of audio-visual content according to claim 1, characterized in that, The determination of the remaining normal battery in the security monitoring device based on the isolation phase matrix and the generation of the current equalization parameter and the transmission power allocation parameter include: Filtering out the remaining normal battery which is not isolated by the fault from the isolation phase matrix, and calculating the state of charge imbalance degree of the remaining normal battery; Based on the state of charge imbalance degree, the equalization current of the remaining normal battery is calculated to obtain the current equalization parameter; According to the state of charge level of the remaining normal battery, the beam weight configuration of the battery management unit in the isolation phase matrix is carried out to obtain the RIS beam enhancement weight; Based on the RIS beam enhancement weight, the transmission power allocation parameter required by the remaining normal battery in the data reporting process is calculated.

10. An adaptive transport system of audio-visual content, characterized in that, The audio and video content adaptive transmission system for executing the audio and video content adaptive transmission method as claimed in any one of claims 1-9, comprising: A creation module for creating a video enhancement unit and a battery management unit based on the audio and video data flow and the battery state data of the security monitoring device; A video signal phase modulation module for performing video signal phase modulation on the monitoring camera in the security monitoring device based on the video enhancement unit to obtain a phase configuration matrix; A null phase adjustment module for adjusting the null phase of the battery management unit based on the battery state data and the phase configuration matrix to obtain an isolation phase matrix; A generation module for determining the remaining normal battery in the security monitoring device based on the isolation phase matrix and generating a current equalization parameter and a transmission power allocation parameter.

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