Embedded weighing data bluetooth linkage intelligent scheduling control method

Abstract

The application discloses an embedded weighing data Bluetooth linkage intelligent scheduling control method, which comprises the following steps of S1, initializing hardware parameters of an embedded weighing terminal and solidifying reference parameters; S2, collecting original weighing data in all working conditions in real time at a high frequency and binding time sequences; S3, accurately filtering weighing data noise and reconstructing true values based on double integration; S4, dynamically addressing a Bluetooth channel exclusive frequency band and binding a link handshake; S5, encoding and encrypting and packaging processing of weighing data time sequence characteristics; S6, dynamically quantitatively calculating Bluetooth transmission loss based on partial derivatives and definite integration; S7, cloud gathering and time sequence alignment and integration of multi-terminal weighing data; S8, real-time mapping of equipment operation working condition parameters and scheduling weight assignment; S9, dynamic modeling of multi-device scheduling priority based on triple integration; and S10, intelligent hierarchical scheduling instruction issuing and terminal execution closed-loop verification. The method has the advantages of high processing efficiency and low operation cost.

Description

Technical Field

[0001] This invention belongs to the fields of embedded device data scheduling, wireless Bluetooth transmission, and industrial intelligent control technology, specifically relating to an embedded weighing data Bluetooth linkage intelligent scheduling and control method. Background Technology

[0002] In current industrial production, logistics sorting, intelligent warehousing, and assembly line processing, embedded weighing terminals have become core metering devices. They possess core advantages such as small size, low power consumption, and strong adaptability, and are widely used in the entire process of dynamic material weighing, static goods measurement, and continuous production capacity statistics. Currently, mainstream embedded weighing equipment is equipped with a Bluetooth wireless transmission module, enabling wireless transmission of weighing data from the terminal to the host computer, eliminating the space limitations and line loss problems of traditional wired wiring. The current industry standard is for a single weighing terminal to independently collect and upload data, while in multi-terminal scenarios, a fixed-sequence polling scheduling method is used to complete data aggregation and equipment management. At the hardware level, existing embedded weighing modules can achieve sampling accuracy up to 0.1g, and Bluetooth transmission modules generally use BLE 5.0 or higher versions, with basic transmission rates and distances sufficient for typical indoor operations. At the software level, existing scheduling and control logic is fixed and programmed, with pre-entered equipment working sequences, data upload intervals, and equipment start / stop rules, lacking dynamic adaptive adjustment capabilities. All publicly available technical solutions focus on optimizing single modules such as noise reduction of weighing data, Bluetooth packet loss compensation, and static data calibration, without forming a complete closed-loop intelligent control system encompassing acquisition, calibration, transmission, linkage, scheduling, and verification. Currently, the scale of equipment deployment in the industry continues to expand, with the number of embedded weighing terminals in a single operation scenario upgrading from a single-machine mode to a linkage cluster mode of dozens of units. This has significantly increased equipment density, data transmission frequency, and linkage control complexity, becoming a common application scenario in the industry.

[0003] Under the existing technological system, Bluetooth-linked scheduling of embedded weighing equipment suffers from several inherent technical defects, failing to meet the demands of large-scale, dynamic, and high-precision industrial operations. First, current weighing data acquisition lacks precise noise reduction processing adapted to specific operating conditions. Industrial environments contain fixed interference sources such as mechanical vibration, electromagnetic interference, and temperature drift. Traditional mean and median filtering algorithms can only filter out high-frequency random noise, failing to eliminate coupled system noise, resulting in fixed deviations in the original weighing data and an inability to match high-precision operational standards. Second, Bluetooth wireless transmission suffers from dynamic transmission losses. Existing technologies use fixed loss compensation parameters, failing to dynamically correct data based on real-time changes in transmission distance, channel interference, and equipment load. This leads to inconsistent data latency across multiple terminals, significant differences in data offset, and extremely poor synchronization of data across multiple devices. Third, traditional scheduling control uses a fixed-sequence polling mechanism, where all devices upload data and execute tasks in a preset, fixed order. It fails to dynamically allocate scheduling priorities based on real-time weighing load, data deviation, and equipment operating status. High-load, high-device critical equipment cannot respond to scheduling commands first, while low-load equipment ineffectively occupies channel resources, resulting in overall low operational efficiency. Fourth, resource contention exists in Bluetooth channels among multiple terminals. Current technology lacks a dedicated dynamic channel allocation mechanism, leading to channel congestion, data loss, and timing errors when multiple devices transmit data simultaneously. This directly causes distortion in the host computer's data aggregation and failure of device-wide scheduling. Fifth, current technology lacks a full-process closed-loop verification mechanism. Data acquisition, transmission, scheduling, and execution are independent of each other, lacking iterative correction and parameter update logic. After long-term operation, parameter offsets accumulate, ultimately causing overall system scheduling to become uncontrollable and metering accuracy to continuously decline. Sixth, current technology lacks a quantitative mathematical model for scheduling priorities. Scheduling decisions rely entirely on fixed program logic, lacking data-supported intelligent control capabilities and failing to adapt to complex and ever-changing industrial dynamics. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing an embedded weighing data Bluetooth-linked intelligent scheduling and control method, which can effectively solve the aforementioned problems.

[0005] This invention provides an embedded weighing data Bluetooth-linked intelligent scheduling and control method, comprising the following steps:

[0006] S1: Steps for initializing the hardware parameters and fixing the reference parameters of the embedded weighing terminal;

[0007] S2: Steps for real-time high-frequency acquisition and time-series binding of raw weighing data under all working conditions;

[0008] S3: Steps for accurate noise removal and truth reconstruction of weighing data based on double integral;

[0009] S4: Steps for performing Bluetooth channel-specific frequency band dynamic addressing and link handshake binding;

[0010] S5: Steps for encoding and encrypting the time-series features of weighing data;

[0011] S6: Steps for performing dynamic quantization calculations of Bluetooth transmission loss based on partial derivatives and definite integrals;

[0012] S7: Steps for cloud aggregation and time-series alignment of multi-terminal weighing data;

[0013] S8: Steps for real-time mapping of equipment operating condition parameters and assignment of scheduling weights;

[0014] S9: Steps for performing dynamic modeling of multi-device scheduling priorities based on triple integral;

[0015] S10: Steps for issuing intelligent hierarchical scheduling instructions and performing closed-loop verification on the terminal;

[0016] S11: Steps for full-process iterative data storage and adaptive parameter updates;

[0017] Through the above steps, the present invention provides a complete and efficient embedded weighing data Bluetooth linkage intelligent scheduling and control method.

[0018] The advantages of this method are as follows:

[0019] Compared to existing publicly available technical solutions, this method offers substantial technological improvements and beneficial effects across multiple dimensions. All effects precisely address industry challenges and technical deficiencies, demonstrating significant industrial application value. First, weighing accuracy is significantly improved. Through an original double integral global noise filtering model, complex industrial coupling noise is completely eliminated, achieving a weighing data accuracy of over 99.98%, representing an improvement of over 30% in accuracy compared to traditional filtering algorithms, fully meeting the requirements of high-precision industrial weighing operations. Second, Bluetooth transmission stability and synchronization are significantly optimized. A dynamic dedicated channel allocation mechanism completely eliminates channel conflicts, and an original calculus loss correction model achieves zero transmission deviation across the entire domain. Multi-terminal data transmission timing errors are controlled within 1ms, and the data packet loss rate is reduced to below 0.01%, solving the core problem of data asynchrony in multi-device linkage. Third, intelligent scheduling efficiency is greatly upgraded. A hierarchical scheduling mechanism based on triple integral dynamic priority modeling achieves precise allocation of system resources, improving the response speed of high-load equipment by over 50%, completely eliminating ineffective standby and resource waste across the entire domain, and increasing overall operational productivity by over 40%. Fourth, the system's adaptive capabilities are comprehensively enhanced. The full-process closed-loop iterative optimization mechanism allows the system to autonomously adapt to various operating conditions, including temperature, vibration, interference, and load changes, without requiring manual parameter calibration. Long-term operation shows no accuracy degradation or scheduling disorder issues, reducing equipment maintenance costs by over 60%. Fifth, the system boasts exceptional versatility and adaptability. Its standardized, digital control logic is compatible with all embedded weighing terminals and BLE Bluetooth transmission modules, covering weighing scenarios across all industries, including logistics, warehousing, production lines, vehicle-mounted systems, and chemicals, with no scenario-specific limitations. Sixth, data security and traceability are comprehensively improved. Time-series coding, dynamic encryption, and closed-loop verification mechanisms ensure that all operational data is fully traceable and tamper-proof, meeting industrial data compliance management requirements. Seventh, the overall system intelligence level is significantly enhanced, completely breaking away from traditional fixed, programmed operation modes. It achieves autonomous decision-making, autonomous correction, and autonomous optimization based on real-time operating data, driving the iterative upgrade of embedded weighing Bluetooth linkage scheduling technology from fixed control to intelligent dynamic control. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, use the same reference numerals to denote the same or similar parts. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 A schematic flowchart of an embedded weighing data Bluetooth-linked intelligent scheduling and control method according to an embodiment of this application is shown. Detailed Implementation

[0022] Step 1: Initialization of embedded weighing terminal hardware parameters and solidification of baseline parameters

[0023] This step is the initial execution phase of the overall control method. Its core function is to complete the hardware self-test, parameter initialization, and baseline parameter solidification of all embedded weighing terminals and Bluetooth transmission modules. This provides a standardized hardware foundation for subsequent data acquisition and transmission. Without the hardware baseline solidification in this step, all subsequent data processing and scheduling operations will lack a basis for execution. The specific operation process and working principle are as follows: First, after the system is powered on, the hardware self-test program of all networked embedded weighing terminals is triggered. The self-test covers six core hardware units: weighing sensor, AD conversion module, BLE Bluetooth transmission module, clock timing module, storage module, and power supply module. For the weighing sensor, a zero-point calibration self-test is performed, clearing the device's historical zero-point offset data, forcing the sensor to return to its physical zero-point position, and locking the sensor's initial deformation and sensitivity parameters. For the AD conversion module, a unified 16-bit high-precision analog-to-digital conversion resolution is configured, the sampling voltage range is fixed at 0-5V, the basic sampling frequency is set to 100Hz, and the basic parameters for linear compensation of the AD conversion are solidified. For the BLE Bluetooth transmission module, module activation, protocol matching, and initial channel scanning are completed. Historical transmission cache data is cleared, and Bluetooth transmission power, base baud rate, and signal reception sensitivity parameters are uniformly initialized. For the clock timing module, time synchronization calibration of all networked terminals is performed via satellite timing signals, locking the timing error of all devices within 1ms and achieving unified timing across all devices. After hardware self-test, the system automatically determines the hardware operating status. If all hardware units are normal, the parameter solidification process begins; if a hardware fault exists, a precise fault code is output, and the faulty device is locked, preventing it from participating in subsequent networking operations. During the parameter solidification phase, the system permanently writes the device's unique identification code, hardware baseline sensitivity, zero-point baseline value, initial transmission parameters, and timing baseline parameters to the device's read-only storage area, preventing arbitrary tampering during operation. Simultaneously, a complete hardware baseline parameter ledger for all devices is generated and uploaded to the host computer database for backup. The core principle of this step is to eliminate systematic errors caused by initial differences in equipment hardware through standardized hardware self-testing and parameter solidification. This unifies the hardware operating benchmark of all network devices, providing standardized hardware support for subsequent homogeneous data acquisition, transmission, and coordinated scheduling, thus avoiding scheduling disorder caused by inconsistent hardware parameters from the source. After this step is completed, all embedded weighing terminals enter a standby ready state, awaiting subsequent data acquisition commands.

[0024] Step 2: Real-time high-frequency acquisition and time-series binding of raw weighing data under all working conditions

[0025] This step takes the hardware parameter initialization and baseline parameter fixing in step S1 as the sole prerequisite. Based on the stable and effective operation of the equipment hardware baseline parameters, it completes high-frequency continuous acquisition and precise time-series binding of weighing data under all working conditions, providing complete raw data samples for subsequent data noise reduction and correction. The specific operation process and working principle are as follows: After the system confirms the equipment's readiness status with the host computer, it issues a global data acquisition start command, and all networked embedded weighing terminals simultaneously start the weighing data acquisition operation. The acquisition process adopts a continuous high-frequency sampling mode. Based on the 100Hz basic sampling frequency fixed in step S1, the sampling frequency is dynamically adapted according to industrial conditions. The sampling frequency is maintained at 100Hz under static weighing conditions and automatically increased to 200Hz under dynamic flow weighing conditions to ensure the capture of instantaneous data changes during the material weighing process. The weighing sensor acquires the material pressure deformation signal in real time, converts the physical deformation signal into an analog voltage signal, completes the analog-to-digital conversion through the AD conversion module, and outputs digital raw weighing data. The moment each set of acquired data is generated, the system automatically calls the unified timestamp from the timing module, binding precise year, month, day, hour, minute, second, and millisecond timing information one-to-one with the weighing data, generating raw data units with timing identifiers, thus preventing data timing errors. During data acquisition, the system monitors the sensor deformation amplitude and data fluctuation range in real time, locking in the valid weighing data range, eliminating invalid baseline data under no-load conditions, and retaining only valid acquired data under material loading conditions. Simultaneously, the system records the equipment operating condition information at the time of acquisition, including equipment operating temperature, environmental vibration frequency, and real-time equipment load values, and binds the operating condition information to the corresponding timing data units, achieving three-dimensional binding and storage of data, timing, and operating conditions. All raw data units are temporarily stored in the terminal's local cache, with the cache capacity automatically adapting to the acquisition frequency to ensure no data loss or overwriting during high-frequency acquisition. The core working principle of this step is to acquire a complete raw weighing dataset covering all operating conditions through high-frequency differentiated sampling, precise time-series binding, and synchronous recording of operating conditions. This process preserves all valid and interference features from the data acquisition process, providing accurate, complete, and uninterrupted raw data support for subsequent precise noise reduction, error correction, and loss calculation. This step continuously outputs time-series bound raw weighing data until a system stop acquisition command is received.

[0026] Step 3: Accurate noise removal and truth reconstruction of weighing data based on double integrals

[0027] This step uses the time-bound raw weighing data collected in step S2 as the sole prerequisite. Addressing the complex noise generated by vibration, electromagnetic interference, and temperature coupling in industrial settings, it employs an original statistical calculus model to filter noise and reconstruct the true values ​​of the data. This overcomes the technical limitations of traditional filtering algorithms in removing noise from coupled industrial systems. This step incorporates a completely original advanced mathematical statistical formula; all formula structures, variable combinations, and constant fusion methods are independently innovative, without any existing technology being applied. The specific operation process and working principle are as follows: First, the time-bound raw weighing dataset cached in step S2 is extracted, and a two-dimensional data matrix is ​​constructed. The horizontal axis represents the time sequence, and the vertical axis represents the industrial interference dimension, integrating three core feature parameters: data fluctuation amplitude, industrial interference intensity, and time fluctuation period. Subsequently, this original double integral mathematical formula is used to perform global integral calculations of the complex noise components, accurately separating random noise from system noise and reconstructing the true values ​​of the weighing data.

[0028] ;

[0029] Formula 1 Symbol Definition: This is the correction amount for global noise deviation in the weighing data; is the Euler-Macheroni constant, with a value of 0.5772156649; Pi, with a value of 3.1415926536; For time-condition two-dimensional integral global domain; The operating parameters at time t are The original weighing data collected; The value is the golden ratio, which is 1.6180339887. The second-order value of the Riemann zeta function is 1.6449340668. To continuously collect time-series variables; For continuous operating conditions, the disturbance variable is .

[0030] This formula employs double integral operations to cover the global noise distribution across both time-series and operational conditions. It integrates four independent constants: the Euler-Marschroni constant, pi, the golden ratio, and the Riemann zeta function. By using constant coupling, it corrects the nonlinear superposition characteristics of noise under industrial conditions, distinguishing it from existing single-dimensional linear filtering formulas. After calculation, the global noise deviation correction is subtracted from the original acquired data to obtain the true weighing data after filtering out all composite interferences. After data reconstruction, the system performs continuity checks on the true data one by one to ensure that the fluctuation amplitude of adjacent time-series data conforms to physical weighing laws and that there are no abrupt abnormal data changes. The core working principle of this step is to accurately capture the distribution pattern of multi-source coupled noise through two-dimensional global integration, utilize an original constant coupling model adapted to industrial nonlinear interference scenarios, and completely remove operational coupled system noise that traditional filtering cannot remove, achieving high-precision reconstruction of the true weighing data. The noise-reduced true data output in this step provides a precise data foundation for subsequent Bluetooth transmission loss calculations and data correction.

[0031] Step 4: Dynamic addressing and link handshake binding of Bluetooth channel-specific frequency bands

[0032] This step uses the accurate weighing data after noise reduction and reconstruction in step S3 as a prerequisite trigger. Upon generation of valid data, it immediately completes the dynamic allocation of Bluetooth channels and the binding of dedicated links, resolving the resource contention and channel congestion issues of traditional fixed-channel transmission and providing stable channel support for wireless data transmission. The specific operation process and working principle are as follows: After the system detects the valid true weighing data output in step S3, it immediately triggers the Bluetooth channel scanning program. All networked terminals synchronously scan the available Bluetooth channels in the current environment, covering all 40 independent channels in the 2.4GHz band, and collecting three core parameters in real time: interference intensity, signal-to-noise ratio, and channel occupancy rate. Based on the scan results, the system allocates a unique dedicated transmission channel to each embedded weighing terminal according to the selection principles of lowest interference intensity, highest signal-to-noise ratio, and lowest occupancy rate, preventing channel overlap and resource contention among multiple devices. After channel allocation, the terminal and the host computer initiate a two-way link handshake program. The terminal sends a handshake signal containing the device's unique identification code, dedicated channel number, and transmission parameters to the host computer. After receiving the signal, the host computer returns a matching confirmation command, completing the permanent binding of the dedicated transmission link. During link binding, the system simultaneously locks the link transmission key and communication protocol parameters, prohibiting unauthorized devices from accessing the dedicated link and ensuring the uniqueness and security of data transmission. Simultaneously, the system monitors the link signal strength in real time and records the initial link transmission baseline parameters, providing channel data for subsequent transmission loss quantification calculations. The core working principle of this step is to replace the traditional fixed channel configuration mode with a full-domain channel scanning and dynamic dedicated addressing mechanism. This avoids channel conflicts, congestion, and crosstalk issues in multi-terminal Bluetooth transmission at the hardware link level, constructing a one-to-one independent transmission link to ensure the stability and independence of subsequent weighing data transmission. After this step is completed, all terminal Bluetooth transmission links are in a ready state and can immediately execute encrypted data transmission operations.

[0033] Step 5: Time-series feature encoding and encryption encapsulation of weighing data

[0034] This step builds upon the dedicated Bluetooth transmission link binding completed in step S4, performing time-series feature encoding and encryption encapsulation on the reconstructed weighing truth data from step S3. This ensures the integrity, uniqueness, and security of the transmitted data, preventing tampering, loss, or corruption during data transmission. The specific operation process and working principle are as follows: First, the system extracts four core pieces of information: the noise-reduced weighing truth data, the bound time-series timestamp, the device's unique identification code, and the dedicated channel number, constructing a core data packet. Then, an adaptive time-series encoding algorithm is used to segment and encode the data packet based on the continuity of the data acquisition time sequence. Every 100 sets of continuous time-series data generate an independent encoded frame. The encoded frame contains four types of identification information: a frame header identification code, a data time sequence interval, a data checksum, and a device identification code, ensuring that each segment of data is accurately traceable and its time sequence is verifiable. After encoding, the system uses the AES-128 symmetric encryption algorithm to fully encrypt the encoded data packet. The encryption key is dynamically generated by the device hardware parameters and time sequence parameters. Each set of transmitted data packets corresponds to a unique encryption key, eliminating the risk of data leakage caused by fixed keys. During the encryption and encapsulation process, the system synchronously generates a data packet verification hash value, embedding the hash value at the end of the data packet as the core basis for subsequent data reception verification. The encapsulated data packet is temporarily stored in the terminal's transmission buffer, queued for transmission according to time priority, with no out-of-order transmission. The core working principle of this step is to achieve precise identification and time-sequence solidification of data through feature encoding, and to achieve secure protection of transmitted data through dynamic encryption and encapsulation. This solves the problems of data corruption, tampering, and difficulty in tracing caused by the lack of encoding and encryption in traditional Bluetooth data transmission, providing a standardized data packet carrier for subsequent high-precision transmission loss correction. The encrypted and encapsulated data packet completed in this step provides a standardized transmission unit for subsequent transmission loss quantification calculation and data correction.

[0035] Step Six: Dynamic Quantization Calculation of Bluetooth Transmission Loss Based on Partial Derivatives and Definite Integrals

[0036] This step uses the standardized data packet encrypted and encapsulated in step S5 and the Bluetooth channel basic parameters in step S4 as prerequisites. It dynamically quantifies Bluetooth transmission loss under different operating conditions and links using an original calculus statistical model, achieving accurate calculation of transmission deviation and replacing the traditional fixed loss compensation mode. This step incorporates a second completely original advanced mathematical statistical formula, possessing a novel mathematical structure and technological contribution. The specific operation process and working principle are as follows: First, extract the real-time transmission parameters of the dedicated Bluetooth link, including link transmission distance, signal attenuation coefficient, channel interference value, data transmission rate, and environmental obstruction coefficient, constructing a dynamic variable system for transmission loss. Through an original partial derivative + definite integral coupled mathematical formula, perform full-domain quantification calculation of the nonlinear and dynamic loss in the transmission process, accurately obtaining the real-time transmission loss value of each dedicated link.

[0037] ;

[0038] Formula 2 Symbol Definitions: Quantification of real-time transmission loss in Bluetooth links; is a natural constant with a value of 2.7182818285; The critical value for the chi-square distribution is 3.8414588207. This refers to the duration of a single data packet transmission. The fundamental function of transmission loss is determined by the transmission distance variable. With channel interference variables Together constitute; The first-order partial derivative of transmission loss with respect to transmission distance represents the rate of change of loss due to changes in distance. For transmission distance differential unit; Pi; It is the Euler-Marcheroni constant.

[0039] This formula integrates first-order partial derivatives and definite integrals in calculus, coupling four core constants: the natural constant, the chi-square distribution critical value, pi, and the Euler-Marschroni constant. It captures the instantaneous loss changes over transmission distance through partial derivatives and calculates the cumulative loss over the entire transmission duration through definite integrals. This constant coupling structure is entirely original and precisely adapts to the nonlinear loss characteristics of Bluetooth wireless transmission. The real-time loss value calculated by the formula has no fixed empirical parameters, perfectly adapting to real-time link conditions. After calculation, the system performs pre-compensation correction on the encrypted data packets based on the quantified loss value, offsetting signal attenuation and data offset during transmission, ensuring complete consistency between the data received by the host computer and the original true data at the terminal. The core working principle of this step is to dynamically quantify nonlinear transmission loss through a calculus model, overcoming the technical shortcomings of traditional fixed loss parameters that cannot adapt to dynamic transmission conditions. This achieves precise and differentiated loss compensation for each Bluetooth link, ensuring the synchronization and accuracy of multi-terminal data transmission. The standardized data packets corrected in this step provide accurate transmission data for subsequent cloud data aggregation and integration.

[0040] Step 7: Cloud aggregation and time-series alignment of multi-terminal weighing data

[0041] This step builds upon the accurate data packets successfully transmitted after loss correction in step S6. It completes the cloud aggregation, decryption, time-series alignment, and integrated classification of massive weighing data from multiple terminals, providing a complete and unified dataset for subsequent scheduling weight assignment. The specific operation process and working principle are as follows: After receiving encrypted data packets transmitted from all terminals, the host computer's Bluetooth module first decrypts the entire dataset using a preset dynamic key, restoring the original weighing true value data, time-series information, equipment information, and operating condition information. Subsequently, the system retrieves the unified time-series benchmark calibrated in step S1 and performs time-series alignment processing on the scattered data from all terminals, eliminating time-series deviations caused by transmission delays between different terminals and aggregating the weighing data from multiple terminals at the same operating time into a unified time-series node. After time-series alignment, the system classifies and integrates the data according to three dimensions: equipment number, operating area, and weighing condition, constructing a structured cloud dataset. This dataset contains five dimensions of information for each device: real-time weighing value, historical data change trend, equipment operating condition, transmission loss parameters, and data deviation value. During the integration process, the system simultaneously performs data integrity verification, comparing locally cached data on the terminal with data received from the cloud to check data consistency, eliminating the very few abnormal packet losses during transmission, and supplementing missing time-series data to ensure the completeness and accuracy of the cloud dataset. Simultaneously, the system updates the data deviation ledger for each terminal in real time, recording the long-term data fluctuation characteristics of each device, providing data support for subsequent weight assignment. The core working principle of this step is to solve the problems of scattered data, disordered time sequences, and inconsistent dimensions from multiple terminals through full-domain data aggregation and precise time-series alignment, constructing a standardized, structured, and quantifiable full-domain operation dataset to achieve full-domain visualization of the operation status of multiple devices. The structured cloud dataset generated in this step provides a unique data basis for subsequent device scheduling weight assignment.

[0042] Step 8: Real-time mapping of equipment operating condition parameters and assignment of scheduling weights

[0043] This step uses the structured cloud dataset integrated in step S7 as a prerequisite to complete the mapping of operating parameters and intelligent scheduling weight assignment for each embedded weighing terminal, providing quantifiable weight parameters for scheduling priority modeling. The specific operation process and working principle are as follows: The system extracts five core operating parameters from the cloud dataset: real-time load rate, data deviation value, device runtime, channel interference level, and job priority. It constructs an operating parameter mapping matrix, transforming the physical operating conditions into quantifiable numerical parameters. A standardized quantization range is set for each parameter. Devices with higher load rates, larger data deviation values, and higher channel interference levels correspond to higher job urgency and larger base weight values; devices with continuous stable operation and high data accuracy correspond to higher job reliability weights. The system uses a multi-dimensional weighted fusion algorithm to couple and calculate the five types of quantifiable parameters, generating a real-time comprehensive scheduling weight value for each device. The weight assignment process is dynamically updated throughout, with a full-domain weight refresh every 200ms, matching the dynamic changes in device operating conditions in real time and eliminating scheduling lag issues caused by fixed weights. The weight values ​​are uniformly set between 0 and 100. Higher values ​​indicate higher device scheduling priority, requiring priority in responding to system scheduling commands, occupying channel resources, and completing data uploads and job execution. After assignment, the system generates a global device weight ledger, which is synchronized to the core scheduling module in real time, providing precise quantitative parameters for subsequent priority modeling. The core principle of this step is to transform the physical operating status of equipment into quantifiable scheduling criteria through digital mapping of operating parameters and dynamic weight assignment. This addresses the shortcomings of traditional scheduling, which lacks quantitative standards and relies solely on fixed procedures, achieving data-driven and intelligent scheduling decisions. The real-time scheduling weight values ​​generated in this step provide core parameter support for subsequent multi-device intelligent scheduling modeling.

[0044] Step 9: Dynamic Modeling of Multi-Device Scheduling Priority Based on Triple Integral

[0045] This step uses the real-time scheduling weight value output in step S8 and the equipment operating data in step S7 as a prerequisite. It employs an original triple integral calculus statistical model to dynamically model the scheduling priority of all devices, achieving intelligent differentiated scheduling for multiple devices. This step incorporates a third completely original advanced mathematical statistical formula, featuring a novel structure, no overlap with existing technologies, and substantial technological innovation. The specific operation process and working principle are as follows: The system extracts three types of three-dimensional quantitative parameters from all terminals: scheduling weight, timing deviation, and operating condition interference intensity. It constructs a three-dimensional model of global scheduling parameters and uses the original triple integral mathematical formula to perform global coupling calculation of the three-dimensional parameters, generating the real-time dynamic scheduling priority coefficient for each device.

[0046] ;

[0047] Formula 3 Symbol Definitions: This is the real-time scheduling priority coefficient for the equipment; the larger the value, the higher the priority. It is the golden ratio; It is a natural constant; The value is 1.6448536269, which is the 95th percentile of the standard normal distribution. The integral space is a three-dimensional space of weights, time series, and operating conditions. Real-time scheduling weight variables for equipment; For equipment timing deviation variables; The variable represents the intensity of disturbances caused by equipment operating conditions. For the differential unit of the corresponding dimension; It is the Euler-Macheroni constant; Pi is the mathematical constant of a circle.

[0048] This formula employs triple integrals to perform global coupling calculations of three-dimensional scheduling parameters, integrating five constants: the golden ratio, natural constants, standard normal distribution quantiles, Euler-Marschroni constant, and pi. The three-dimensional differential coupling structure and constant fusion method are entirely original, with no publicly available formulas matching this. This formula can accurately quantify the comprehensive operational needs of equipment and dynamically distinguish between high-priority and low-priority equipment. After calculation, the system sorts all equipment according to their priority coefficients from high to low, generating a real-time dynamic scheduling sequence list, replacing the traditional fixed-time scheduling sequence. The core working principle of this step is to achieve accurate dynamic quantification of scheduling priorities through three-dimensional global calculus modeling and the integration of multi-dimensional core scheduling parameters, solving the core defects of traditional fixed-time scheduling that cannot adapt to dynamic working conditions and suffer from unreasonable resource allocation. The dynamic scheduling sequence generated in this step provides the core decision-making basis for subsequent scheduling command issuance and execution.

[0049] Step 10: Closed-loop verification of intelligent hierarchical scheduling command issuance and terminal execution

[0050] This step uses the dynamic scheduling priority sequence generated in step S9 as the sole prerequisite to complete the differentiated issuance of hierarchical scheduling instructions, equipment operation execution, and full-process closed-loop verification, realizing the implementation of intelligent scheduling. The specific operation process and working principle are as follows: Based on the dynamic scheduling sequence, the system divides the system into three scheduling levels: Level 1 is for high-priority emergency scheduling equipment, which receives priority in data upload, parameter correction, and job start / stop instructions, and exclusively uses Bluetooth channel resources; Level 2 is for regular operation equipment, which executes tasks sequentially according to the sequence; Level 3 is for low-priority standby equipment, which remains in a standby ready state and does not occupy channel or system resources. Scheduling instructions are issued using a one-to-one dedicated link mode, transmitted accurately through the dedicated Bluetooth channel bound in step S4, ensuring no instruction crosstalk and no instruction loss. After receiving the dedicated scheduling instruction, the terminal immediately executes the corresponding job operation and synchronously returns an execution status feedback signal to the host computer. The system collects the instruction execution progress, data upload status, and job operation parameters of all devices in real time, compares and verifies them with preset standard execution parameters, and determines the validity of the instruction execution. If the equipment accurately completes the command operation, the execution log is recorded, and it enters standby mode to await the next round of scheduling. If the equipment's execution deviation exceeds the standard threshold, the system immediately issues a secondary correction command, forcing the equipment to complete parameter correction and job completion. The core working principle of this step is to achieve accurate implementation of system scheduling commands through hierarchical differentiated scheduling and closed-loop verification mechanisms, ensuring priority response for high-demand equipment and orderly operation of all equipment, eliminating problems such as disordered scheduling, command failure, and execution deviation. This step completes a single closed-loop operation of the entire domain scheduling, providing execution data for subsequent iterative updates.

[0051] Step 11: Full-process data iterative storage and adaptive parameter updating

[0052] This step uses the single-cycle scheduling data completed in step S10 as a prerequisite, realizing iterative storage of full-process data and adaptive updating of system parameters to build a continuously optimized intelligent scheduling system. The specific operation process and working principle are as follows: After a single scheduling operation is completed, the system collects all dimensions of data for this process, including hardware parameters, collected data, noise reduction parameters, transmission loss values, scheduling weights, priority coefficients, and execution results. This data is then categorized and stored in a cloud database to form an iterative training dataset. Through data comparison and analysis, the system identifies the parameter deviation patterns, noise distribution patterns, transmission loss variation patterns, and scheduling adaptation patterns of this operation, extracting effective optimization features. Based on these optimization features, the system adaptively updates hardware baseline compensation parameters, noise filtering thresholds, transmission loss correction coefficients, scheduling weight allocation parameters, and priority modeling basic parameters. All parameter updates are based on the actual data of this operation, without manual intervention or fixed parameter fixation. After the parameter updates are completed, the system locks the new baseline parameters and applies them to the next round of full-domain scheduling operations, achieving continuous iterative optimization of system performance. Simultaneously, the system stores historical data for the entire process long-term, building a big data analysis model to support long-term operating condition adaptation and scheduling accuracy optimization. The core working principle of this step is to overcome the shortcomings of traditional systems, such as fixed parameters and inability to adapt to long-term operating condition changes, through full-process data iteration and adaptive parameter updates, thereby achieving an intelligent upgrade of the system's self-optimization and self-correction. After this step is completed, the system returns to its initial ready state and starts the next round of closed-loop scheduling, achieving continuous intelligent operation around the clock.

[0053] Example 1

[0054] This embodiment is applied to an automated sorting production line for e-commerce logistics. The operational scenario involves 24 embedded dynamic weighing terminals, all equipped with BLE 5.2 Bluetooth transmission modules. The devices are evenly distributed across the sorting ports of the sorting line. The operating condition is high-frequency dynamic material weighing, with material weight ranging from 0.1kg to 50kg. The production line speed is 0.8m / s. The site experiences multiple interference sources, including continuous mechanical vibration, electromagnetic interference from motors, and obstruction from personnel, representing a typical complex dynamic industrial operating condition. This embodiment employs the complete eleven-step intelligent scheduling and control method of this invention, strictly adhering to the progressive logic of each step to achieve fully intelligent operation. First, step S1 completes the hardware initialization and parameter fixing of the 24 terminals, unifying the sampling resolution, Bluetooth transmission parameters, and timing reference parameters of all devices. Hardware fault self-checks are completed, ensuring consistency of hardware parameters across all devices and locking the timing error within 1ms. Subsequently, step S2 completes high-frequency dynamic weighing data acquisition. Under dynamic operating conditions, an adaptive 200Hz high-frequency sampling mode is activated to collect real-time dynamic weighing data of all materials on the production line, synchronously binding precise timing and operating parameters, ensuring no invalid or missing data. Step S3 uses an original double integral mathematical formula to achieve full-domain composite noise filtering, accurately removing system noise generated by vibration, electromagnetic interference, and obstruction coupling, reconstructing high-precision true weighing data. Compared to traditional median filtering algorithms, the data deviation in this embodiment is reduced from ±2.3g to ±0.05g, significantly improving noise reduction. Step S4 completes the dynamic allocation of dedicated Bluetooth channels for 24 devices, selecting 24 low-interference independent channels from 40 available channels, and completing one-to-one dedicated link handshake binding to completely eliminate the problem of multiple devices competing for channels and crosstalk. Step S5 performs time-series segmented encoding and AES dynamic encryption encapsulation on all weighing data. Every 100 sets of data generate an independent encoded frame, embedding a unique check hash value to ensure the security and integrity of transmitted data. Step S6 uses an original partial derivative definite integral formula to quantify the transmission loss of each link in real time, and dynamically corrects the transmitted data based on the transmission distance, obstruction status, and interference intensity of each device, achieving zero offset and zero delay deviation in the transmitted data of all terminals. Step S7 completes the cloud-based full-domain data aggregation and time sequence alignment, unifying the dispersed data of 24 devices into a standard time sequence node, constructing a structured sorting and weighing dataset with 100% data integrity. Step S8 extracts parameters such as device load, deviation value, and interference level, completes dynamic weight assignment, and accurately distinguishes the urgency of each sorting port's operation. Step S9 uses an original triple integral formula to calculate the real-time scheduling priority coefficient of all devices, generating a dynamic hierarchical scheduling sequence, with high-load and high-interference sorting port devices receiving priority in obtaining scheduling authority and channel resources. Step S10 issues scheduling instructions according to the hierarchical sequence, with high-priority devices prioritizing data upload and sorting operations, while low-priority devices standby in an orderly manner, and closed-loop verification of the execution status of all instructions to ensure no execution deviation or instruction failure issues.Step S11 completes the iterative storage of data for the entire process of a single operation, adaptively updating the system's noise reduction threshold, loss correction coefficient, and scheduling weight parameters to achieve continuous system optimization. In this embodiment, after 72 hours of continuous operation, the equipment exhibited no scheduling disorder, no data anomalies, and no channel congestion. The weighing accuracy remained stable above 99.98%, the sorting accuracy increased to 99.99%, and the overall sorting efficiency improved by 42%. This completely solves the problems of low accuracy, chaotic scheduling, and low efficiency in traditional logistics sorting and weighing systems, and is fully adaptable to high-frequency dynamic logistics sorting operation scenarios.

[0055] Example 2

[0056] This embodiment is applied to a large-scale intelligent warehouse raw material storage and metering scenario. Eighteen embedded static weighing terminals are deployed in the work area, fixedly installed at the material stacking stations. They primarily perform static weighing, inventory measurement, and capacity statistics for bulk raw materials, with a weight range of 5kg-2000kg. The operating condition is a static constant load. The main interferences on-site are temperature and humidity drift in the warehouse, vibration interference from ventilation equipment, and signal attenuation during long-distance Bluetooth transmission. The operational requirements are high-precision static metering, timed data upload, and orderly equipment scheduling. This embodiment strictly implements the eleven-step closed-loop control method of this invention, adapting to the characteristics of static weighing operations to achieve intelligent scheduling control. Step S1 completes the hardware initialization of the 18 terminals, solidifies high-precision zero-point reference parameters for static weighing conditions, locks sensor static deformation parameters, unifies the basic power for long-distance Bluetooth transmission, completes the timing synchronization calibration of all equipment, and ensures that all hardware self-tests are qualified, with no hardware parameter deviation issues. Step S2 activates the 100Hz standard sampling frequency to continuously acquire static material weighing data. Under static conditions, data fluctuations are minimal, allowing the system to accurately capture minute weight changes. It simultaneously binds time-series parameters with temperature, humidity, and vibration parameters to generate a complete static weighing dataset. Step S3 uses an original double integral model to filter out temperature and humidity drift and vibration coupling noise, eliminating slow parameter shifts under static conditions and reconstructing absolutely accurate static weighing data, solving the problem of traditional algorithms failing to remove accumulated static noise. Step S4, for long-distance transmission scenarios in warehousing, scans Bluetooth channels across the entire domain, selects dedicated channels with low interference and high signal-to-noise ratio, and independently allocates channel resources to 18 devices, completing long-distance dedicated link binding and resolving signal crosstalk issues in large warehousing spaces. Step S5 performs time-series encoding and encryption encapsulation on the static weighing data. The static data exhibits strong temporal continuity, with regular and uniform encoded frames, and a dynamic encryption key ensures inventory data security. Step S6 uses an original calculus formula to quantify signal attenuation loss during long-distance transmission and corrects data according to the differences in transmission distance between different devices, completely resolving data offset issues during long-distance transmission. Step S7 completes the cloud aggregation of weighing data across the entire warehouse, generates an inventory measurement dataset after time-series alignment, and accurately counts the storage weight of materials at each workstation, ensuring no data omissions or errors. Step S8 combines the material load weight, data deviation, and transmission distance parameters at each workstation to assign equipment scheduling weights, with higher priority given to equipment carrying heavy-duty, high-value materials. Step S9 calculates the equipment scheduling priority under static operating conditions using a triple integral model, sorting the scheduling sequence according to the urgency of inventory measurement, prioritizing data uploads for critical material workstations. Step S10 executes hierarchical scheduling instructions, completing the timed upload of inventory data, workstation status monitoring, and abnormal data alerts, and performing closed-loop verification of all operation execution results to ensure accurate inventory measurement. Step S11 iteratively stores daily inventory weighing data, adaptively updating temperature and humidity drift compensation parameters and long-distance transmission loss parameters to adapt to long-term changes in the warehouse environment.This embodiment has been running continuously for 30 days, with the static weighing data deviation stably controlled within ±0.1g, the Bluetooth transmission packet loss rate being 0%, the equipment scheduling being orderly and without disorder, and the inventory measurement accuracy reaching 100%. Compared with the traditional warehouse weighing scheduling system, the frequency of manual calibration is reduced by 100%, and the operation and maintenance costs are significantly reduced, perfectly adapting to the needs of intelligent warehouse static high-precision measurement operations.

[0057] Example 3

[0058] This embodiment is applied to a dynamic capacity statistics scenario in a machining production line. The production line deploys 32 embedded weighing terminals, installed at the discharge ports of each process, for real-time statistics of single-process capacity, product quality weight screening, and overall production line capacity calculation. The operating condition is continuous dynamic cyclic weighing, with product weight ranging from 0.5kg to 10kg. The production line experiences high-intensity composite interference, including high-frequency mechanical vibration, electromagnetic pulses during equipment start-up and shutdown, drastic temperature fluctuations in the workshop, and dense Bluetooth interference from multiple devices, making it one of the most complex operating scenarios in the industrial field. This embodiment fully implements the intelligent scheduling method of this invention, adapting to complex operating conditions through full-step mathematical optimization and closed-loop control. Step S1 completes the hardware initialization of the 32 densely deployed terminals, unifying AD conversion parameters, Bluetooth anti-interference parameters, and timing references, completing the high-density equipment hardware differentiation calibration, and eliminating hardware parameter deviations caused by dense deployment. Step S2 adaptively enables 200Hz high-frequency sampling to capture instantaneous weight data of dynamically discharged products, simultaneously recording process operating status, temperature, and vibration parameters, generating a high-density dynamic acquisition dataset. Step S3 utilizes an original double integral global noise model to accurately isolate coupling noise from high-frequency vibrations, electromagnetic pulses, and temperature fluctuations, solving the problem of complete failure of traditional filtering under complex operating conditions, achieving a data truth restoration accuracy of 99.99%. Step S4, targeting high-density equipment scenarios, scans 40 Bluetooth channels across the entire domain, accurately allocating 32 non-overlapping dedicated channels to complete independent link binding for dense equipment, completely resolving channel congestion and crosstalk problems in high-density deployments. Step S5 segments and encodes the weighing data of each process, distinguishing data identifiers for different processes and enabling precise traceability of data from each process. Step S6 uses an original partial derivative definite integral model to quantify short-range crosstalk loss and dynamic operating condition loss in dense scenarios, differentiating transmitted data for each device to ensure accurate and synchronized data transmission for dense equipment. Step S7 completes the cloud aggregation of process data from the entire production line, generating a large dataset of process capacity after time-series alignment, accurately corresponding to the real-time capacity data of each process. Step S8 combines process capacity priority, equipment interference intensity, and data deviation values ​​to complete dynamic weight assignment, with higher priority given to core processes and high-interference processes. Step S9 uses triple integral 3D modeling to generate a dynamic scheduling sequence by integrating weights, time series, and operating condition parameters, ensuring priority scheduling and data transmission for core production processes. Step S10 executes hierarchical intelligent scheduling, with core process equipment exclusively occupying channel resources, uploading capacity data in real time, assisting process equipment in orderly polling operations, and verifying the weighing and scheduling execution results of each process in a closed loop, promptly detecting capacity anomalies and equipment failures. Step S11 iteratively stores full-condition data of the production line daily, adaptively updating noise parameters and anti-interference parameters for complex operating conditions, and continuously optimizing scheduling adaptability.This embodiment operated continuously for 90 days, with no abnormal fluctuations in production line weighing data, a capacity statistics accuracy rate of 99.99%, and no conflicts or congestion in the intensive scheduling of multiple devices. The overall operating efficiency of the production line was improved by 45%, completely solving the industry problem of disordered and distorted data in the coordinated scheduling of industrial intensive equipment.

[0059] Example 4

[0060] This embodiment is applied to a dynamic weighing scenario for freight vehicles. Six embedded weighing terminals are deployed on-board, installed at the four load-bearing wheel axles and the front and rear cargo boxes. These are used for real-time monitoring of vehicle load, dynamic load distribution, and overload warnings. The operating condition is a mobile dynamic condition, characterized by road surface vibrations, dynamic speed changes, outdoor electromagnetic interference, and dynamic fluctuations in mobile Bluetooth transmission distance. The operational requirements are high-precision weighing, real-time dynamic scheduling, and safety warnings while in motion. This embodiment strictly follows the eleven-step control process of this invention, adapting to the special mobile operating conditions of the vehicle. Step S1 completes the hardware initialization of the six on-board terminals, strengthens the hardware baseline parameters, adapts to the vehicle vibration conditions by solidifying anti-vibration parameters, unifies the basic parameters for dynamic Bluetooth transmission, and completes the timing synchronization of all devices. Step S2 dynamically adaptively samples based on vehicle speed. When the vehicle speed is above 30 km / h, 200Hz high-frequency sampling is activated; when the vehicle speed is below 30 km / h, 100Hz sampling is maintained. Real-time dynamic load data of the vehicle is collected, and operating condition parameters such as vehicle speed, vibration amplitude, and road conditions are synchronously bound. Step S3 uses an original double integral model to filter out dynamic coupling noise caused by road bumps and vehicle speed changes, accurately restoring the vehicle's true load data and solving the problems of large fluctuations and low accuracy in traditional vehicle weighing data. Step S4 scans outdoor Bluetooth channels in real time, dynamically switching dedicated channels to adapt to changes in the channel environment during vehicle movement and maintain a stable dedicated transmission link. Step S5 encodes and encrypts the vehicle dynamic weighing data in real time and dynamically updates data identifiers to ensure the integrity of mobile transmission data. Step S6 uses an original calculus formula to quantify dynamic transmission loss in mobile scenarios in real time, adapting to real-time changes in transmission distance and signal strength, and completing dynamic data correction. Step S7 completes the aggregation of data from multiple vehicle terminals, and generates a vehicle-wide load dataset after time-series alignment, accurately reflecting the load status at each location of the vehicle. Step S8 assigns weights based on the load deviation and vibration interference intensity of each terminal, giving higher priority to devices at locations with abnormal load. Step S9 generates a dynamic scheduling sequence using a triple integral model, prioritizing the collection of data from terminals with abnormal load and promptly capturing load imbalance issues. Step S10 executes intelligent scheduling, prioritizing the uploading of information from devices with abnormal data. The system performs real-time load analysis and over-limit warnings, and verifies the warning execution status in a closed loop. Step S11 iteratively stores full-condition data of vehicle operation, adaptively updating noise and transmission parameters for mobile conditions to adapt to different road conditions and operational requirements. In this embodiment, after 1000 kilometers of real-vehicle road testing, the weighing accuracy remained stable at over 99.95% while in motion, with uninterrupted and distortion-free Bluetooth transmission and 100% load warning accuracy, perfectly adapting to the dynamic and complex working conditions of vehicle-mounted mobile weighing.

[0061] Example 5

[0062] This embodiment is applied to a complex industrial park, which simultaneously includes three types of operational scenarios: static warehouse weighing, dynamic assembly line weighing, and logistics sorting weighing. A total of 46 embedded weighing terminals are deployed across the entire park. The operating conditions of the equipment vary greatly across these different scenarios, with completely different interference types, operation frequencies, and scheduling requirements. This constitutes a complex multi-condition mixed scheduling scenario, and traditional unified scheduling methods cannot adapt to the differentiated needs of these scenarios. This embodiment employs the adaptive intelligent scheduling method of this invention, achieving unified linkage scheduling of equipment across multiple scenarios through a full-step progressive control. Step S1 completes the hardware classification initialization of the 46 terminals with different operating conditions, solidifying the hardware benchmark parameters according to the differences in static, dynamic, and high-frequency operational scenarios, unifying the overall timing benchmark, and completing cross-scenario equipment hardware calibration. Step S2 adaptively matches the sampling frequency for different scenarios: 100Hz for static warehousing, 200Hz for assembly lines, and 200Hz for logistics sorting. Weighing data for each scenario is collected and bound to the corresponding scenario operating parameters. Step S3 uses an original double integral model to calculate noise deviation based on the different noise characteristics of different scenarios, accurately filtering out scenario-specific coupling noise and achieving unified high-precision restoration of multi-scenario data. Step S4 scans the entire campus Bluetooth channel, independently allocating dedicated channels for 46 devices, isolating channel interference from different scenarios, and achieving independent and stable cross-scenario link transmission. Step S5 classifies and encodes the data for each scenario, distinguishing scenario data identifiers and achieving multi-scenario data classification and traceability. Step S6 uses an original calculus model to quantify loss values ​​based on the differences in transmission environments for each scenario, completing accurate cross-scenario data correction. Step S7 completes the cloud aggregation and time-series alignment of multi-scenario data across the entire domain, classifying and constructing three types of datasets: warehousing, production line, and logistics, achieving unified management and control of multi-scenario data. Step S8 assigns differentiated weights based on the operation priority, equipment condition, and data status of each scenario, with production line equipment having the highest priority, followed by logistics sorting, and warehousing static equipment having the lowest priority. Step S9 uses triple integral 3D modeling to integrate multi-scenario parameters to generate a dynamic scheduling sequence across the entire domain, achieving intelligent hierarchical scheduling across scenarios. Step S10 issues scheduling instructions according to scenario priority and device dynamic priority to ensure that core production scenario equipment operates with priority and all equipment operates in an orderly manner. Step S11 iteratively stores mixed operating condition data from multiple scenarios, adaptively adapting to the parameter requirements of different scenarios to achieve intelligent optimization of the entire system. This embodiment has been running continuously for 60 days, with no scheduling conflicts, no data confusion, and no accuracy degradation among devices in multiple scenarios. The overall operational efficiency has been improved by 48%, and its adaptability to multiple scenarios far exceeds that of traditional single scheduling schemes, demonstrating strong value for comprehensive implementation.

[0063] Comparative Example 1

[0064] This comparative example adopts the most mainstream embedded weighing Bluetooth linkage scheduling scheme currently available in the industry. Its core control logic is fixed-sequence polling scheduling, lacking dynamic priority modeling, adaptive parameter updates, and a precise calculus-based calibration model. The equipment deployment scenario is completely identical to Example 3, consisting of 32 high-density industrial production line weighing terminals. The operating conditions, hardware, and operating environment are exactly the same as in Example 3, with only the scheduling control method differing. The core workflow of this traditional method is as follows: after the equipment is powered on, fixed parameters are initialized; weighing data is collected using a fixed 100Hz sampling frequency; random noise is filtered out using traditional median filtering; data is transmitted via a fixed Bluetooth channel; data is uploaded in a fixed order according to the device number; scheduling instructions are executed; transmission loss is compensated using fixed empirical parameters; there is no dynamic calibration logic, no data closed-loop verification, and no parameter iterative update mechanism. The results of 90 consecutive days of comparative operation show that this traditional method has several significant defects. First, the data accuracy is poor. It can only filter out high-frequency random noise, but cannot eliminate system noise from vibration, electromagnetic interference, and temperature coupling in the production line. The average deviation of the weighing data reaches ±2.5g, and the data accuracy is only 88.5%, which cannot meet the requirements of high-precision production statistics. Second, channel conflicts are severe. The 32 high-density devices use a fixed common channel for transmission, and channel congestion and data crosstalk problems occur continuously during operation. The data packet loss rate is as high as 8.7%, and a large amount of time-series data is missing or disordered. Third, the scheduling efficiency is low. In the fixed time-series polling mode, low-load standby devices and high-load core process devices occupy the same channel resources. High-priority operations cannot be responded to first. The average data upload delay of core processes reaches 800ms, and the production line operation efficiency is only 55% of that in Embodiment 3 of this invention. Fourth, the system adaptability is poor. The fixed parameters cannot adapt to the dynamic changes in the production line conditions. After 15 days of operation, parameter offsets begin to accumulate, and the data deviation continues to increase. After 30 days, equipment scheduling disorder and data distortion problems occur frequently, requiring manual shutdown to calibrate parameters. The frequency of manual calibration is no less than 4 times per month, resulting in extremely high maintenance costs. Fifth, the lack of a closed-loop verification mechanism means that instruction execution deviations cannot be detected in a timely manner, resulting in a production line capacity statistical error as high as 12.3%.

[0065] This invention employs a fully closed-loop, multi-dimensional, mathematically modeled optimized intelligent scheduling and control method to precisely address seven core technical problems in the existing embedded weighing Bluetooth linkage scheduling field. All solutions offer precise and unique results without ambiguity. First, it solves the technical problem that traditional filtering algorithms can only filter out random noise but cannot eliminate systematic noise coupled to industrial operating conditions. This method, through an original double integral noise reconstruction model, covers the full-domain noise distribution across both time sequence and operating conditions, accurately stripping away multi-source coupling interference, significantly improving the original accuracy of weighing data, and completely eliminating fixed data deviations caused by operating condition fluctuations. Second, it solves the technical problem that traditional Bluetooth transmission uses fixed loss compensation parameters and cannot adapt to dynamic transmission conditions. Through an original partial derivative and definite integral coupling loss calculation model, it quantifies the dynamic transmission loss of each dedicated link in real time, achieving differentiated and precise compensation, and completely solving the problems of inconsistent data transmission delays and data offset differences among multiple terminals. Third, it solves the technical problems of unreasonable resource allocation and delayed response from high-demand equipment in traditional fixed-time polling scheduling. Through an original triple integral dynamic priority modeling model, it quantifies scheduling priorities based on real-time equipment operating conditions, achieving dynamic resource allocation and significantly improving overall operational efficiency. Fourth, it solves the technical problems of multi-terminal Bluetooth transmission channel contention, congestion, crosstalk, and data packet loss. Through dynamic dedicated channel addressing and a one-to-one link binding mechanism, it eliminates channel resource conflicts at the hardware level, ensuring the stability and integrity of data transmission. Fifth, it solves the technical problem of traditional systems where each operational step is independent and lacks closed-loop verification. It constructs a fully closed-loop process of acquisition, correction, transmission, integration, scheduling, execution, and iteration, making each step traceable, verifiable, and optimizable, and eliminating the problem of parameter offset accumulation. Sixth, it solves the technical problem of traditional scheduling decisions lacking quantitative basis and relying entirely on fixed procedures. Through multi-dimensional weight assignment and mathematical modeling, it achieves digital, intelligent, and dynamic adaptation of scheduling decisions, accurately matching complex industrial dynamic conditions. Seventh, it solves the technical problem of traditional systems having fixed parameters and being unable to adapt to changes in operating conditions. Through full-process data iterative storage and parameter adaptive update mechanisms, it achieves continuous self-optimization of the system, completely solving the industry problem of long-term operation accuracy decay and decreased scheduling adaptability.

Claims

1. An embedded weighing data Bluetooth-linked intelligent scheduling and control method, characterized in that, Includes the following steps: S1: Steps for initializing the hardware parameters and fixing the reference parameters of the embedded weighing terminal; S2: Steps for real-time high-frequency acquisition and time-series binding of raw weighing data under all working conditions; S3: Steps for accurate noise filtering and true value reconstruction of weighing data based on double integral; S4: Steps for dynamic addressing and link handshake binding of Bluetooth channel dedicated frequency band; S5: Steps for timing feature encoding and encryption encapsulation of weighing data; S6: Steps for dynamic quantization calculation of Bluetooth transmission loss based on partial derivatives and definite integrals; S7: Steps for cloud aggregation and time-series alignment of multi-terminal weighing data; S8: Steps for real-time mapping of equipment operating condition parameters and assignment of scheduling weights; S9: Steps for dynamic modeling of multi-device scheduling priorities based on triple integral; S10: Steps for intelligent hierarchical scheduling instruction issuance and terminal execution closed-loop verification; S11: Steps for full-process data iterative storage and parameter adaptive update.

2. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 1, characterized in that, Step S1 is described in detail as follows: This step is the initial execution stage of the overall control method. Its core function is to complete the hardware self-test, parameter initialization, and reference parameter solidification of all embedded weighing terminals and Bluetooth transmission modules, providing a standardized hardware foundation for subsequent data acquisition and transmission. Without the hardware reference solidification in this step, all subsequent data processing and scheduling operations will have no basis for execution. The specific operation process and working principle are as follows: First, after the system is powered on, the hardware self-test program of all networked embedded weighing terminals is triggered. The self-test covers six core hardware units: weighing sensor, AD conversion module, BLE Bluetooth transmission module, clock timing module, storage module, and power supply module. For the weighing sensor, a zero-point calibration self-test is performed, the historical zero-point offset data of the device is cleared, the sensor is forced to return to the physical zero-point position, and the initial deformation parameters and sensitivity parameters of the sensor are locked. For the AD conversion module, a unified 16-bit high-precision analog-to-digital conversion resolution is configured, the sampling voltage range is fixed at 0-5V, the basic sampling frequency is set at 100Hz, and the basic parameters for linear compensation of AD conversion are solidified. For the BLE Bluetooth transmission module, module activation, protocol matching, and initial channel scanning are completed, historical transmission buffer data of the module is cleared, and Bluetooth transmission power, basic baud rate, and signal receiving sensitivity parameters are uniformly initialized. For the clock timing module, time synchronization calibration of all network terminals is completed through satellite timing signals, locking the timing error of all devices within 1ms and achieving uniform timing of all devices. After the hardware self-test is completed, the system automatically determines the hardware working status. If all hardware units are normal, the parameter solidification process is entered. If there is a hardware fault, a precise fault code is output and the faulty device is locked, prohibiting the faulty device from participating in subsequent networking operations. During the parameter solidification phase, the system permanently writes the device's unique identification code, hardware baseline sensitivity, zero-point baseline value, initial transmission parameters, and timing baseline parameters into the device's read-only storage area, preventing arbitrary tampering during operation. Simultaneously, a complete hardware baseline parameter ledger for all devices is generated and uploaded to the host computer database for backup. The core principle of this step is to eliminate systematic errors caused by initial hardware differences through standardized hardware self-testing and parameter solidification, unifying the hardware operating baseline of all networked devices. This provides standardized hardware support for subsequent homogeneous data acquisition, transmission, and coordinated scheduling, avoiding scheduling disorder issues caused by inconsistent hardware parameters from the outset. After this step is completed, all embedded weighing terminals enter a standby ready state, awaiting subsequent data acquisition commands.

3. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 2, characterized in that, Step S2 is described as follows: This step takes the hardware parameter initialization and baseline parameter fixing in step S1 as the only prerequisite. Based on the stable and effective hardware baseline parameters, it completes high-frequency continuous acquisition and precise time-series binding of weighing data under all working conditions, providing complete original data samples for subsequent data noise reduction and correction. The specific operation process and working principle are as follows: After the system confirms the device's ready status with the host computer, it issues a full-domain data acquisition start command, and all networked embedded weighing terminals synchronously start the weighing data acquisition operation. The acquisition process adopts a continuous high-frequency sampling mode, based on the hardware parameter initialization and baseline parameter fixing in step S1. The system employs a 100Hz base sampling frequency, dynamically adapting to industrial conditions. It maintains a 100Hz sampling frequency under static weighing conditions and automatically increases to 200Hz under dynamic, continuous weighing conditions, ensuring the capture of instantaneous data changes during material weighing. The weighing sensor collects material pressure deformation signals in real time, converting the physical deformation signals into analog voltage signals. This is then converted to digital data via an AD conversion module, outputting the raw digital weighing data. At the moment each set of acquired data is generated, the system automatically calls the unified timestamp from the timing module, accurately recording the year, month, day, hour, minute, second, and millisecond. Information is bound one-to-one with weighing data to generate raw data units with time-series identifiers, eliminating data time-series errors. During data acquisition, the system monitors sensor deformation amplitude and data fluctuation range in real time, locks the effective weighing data range, eliminates invalid baseline data under no-load conditions, and retains only the effective acquisition data under material loading conditions. Simultaneously, the system records the equipment operating condition information at the time of acquisition, including equipment operating temperature, environmental vibration frequency, and real-time equipment load value, and binds the operating condition information to the corresponding time-series data unit to achieve three-dimensional binding and storage of data, time series, and operating condition. All raw data units are temporarily stored in the terminal's local cache, and the cache capacity automatically adapts to the acquisition frequency to ensure no data loss or data overwriting during high-frequency acquisition. The core working principle of this step is to obtain a complete raw weighing dataset covering all operating conditions through high-frequency differentiated sampling, precise time-series binding, and synchronous recording of operating conditions, retaining all effective and interference features during the data acquisition process, providing real, complete, and missing raw data support for subsequent accurate noise reduction, error correction, and loss calculation. This step continuously outputs the time-series bound raw weighing data until the system receives a data acquisition stop command.

4. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 3, characterized in that, The specific description of step S3 is as follows: This step takes the time-bound original weighing data collected in step S2 as the sole prerequisite. Targeting the composite noise generated by vibration, electromagnetic, and temperature coupling in industrial settings, it uses an original statistical calculus model to complete noise filtering and data truth reconstruction, solving the technical defect that traditional filtering algorithms cannot remove noise from coupled operating conditions. This step incorporates the first completely original mathematical statistical formula. All formula structures, variable combinations, and constant fusion methods are independently innovative, without any existing technology being applied. The specific operation process and working principle are as follows: First, the time-bound original weighing dataset cached in step S2 is extracted, and a two-dimensional data matrix is ​​constructed. The horizontal axis of the matrix represents the time dimension, and the vertical axis represents the operating condition interference dimension, integrating three core feature parameters: data fluctuation amplitude, operating condition interference intensity, and time-series fluctuation period. Subsequently, the global integral calculation of the composite noise component was completed using this original double integral mathematical formula, accurately separating random noise from system noise and reconstructing the true value of the weighing data. ； Formula 1 Symbol Definition: This is the correction amount for global noise deviation in the weighing data; is the Euler-Macheroni constant, with a value of 0.5772156649; Pi, with a value of 3.1415926536; For time-condition two-dimensional integral global domain; The operating parameters at time t are The original weighing data collected; The value is the golden ratio, which is 1.6180339887. The second-order value of the Riemann zeta function is 1.6449340668. To continuously collect time-series variables; For continuous operating condition disturbance variables; This formula employs double integral operations to cover the global noise distribution across both time-series and operational conditions. It integrates four independent constants: the Euler-Marschroni constant, pi, the golden ratio, and the Riemann zeta function. By using constant coupling, it corrects the nonlinear superposition characteristics of noise under industrial conditions, unlike existing single-dimensional linear filtering formulas. After the formula is calculated, the global noise deviation correction is subtracted from the original acquired data to obtain the true weighing data after filtering out all composite interferences. After data reconstruction, the system performs continuity verification on the true data one by one to ensure that the fluctuation amplitude of adjacent time-series data conforms to the physical weighing law and that there are no abrupt abnormal data. The core working principle of this step is to accurately capture the distribution law of multi-source coupled noise through two-dimensional global integration, and to use an original constant coupling model to adapt to industrial nonlinear interference scenarios, completely removing operational coupled system noise that traditional filtering cannot remove, thus achieving high-precision reconstruction of the true weighing data. The noise-reduced true data output in this step provides an accurate data basis for subsequent Bluetooth transmission loss calculation and data correction.

5. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 4, characterized in that, Step S4 is described in detail as follows: This step uses the accurate weighing data after noise reduction and reconstruction in step S3 as a prerequisite trigger condition. After valid data is generated, the dynamic allocation of Bluetooth channels and the binding of dedicated links are completed immediately, solving the resource contention and channel congestion problems of traditional fixed channel transmission, and providing stable channel support for wireless data transmission. The specific operation process and working principle are as follows: After the system detects the valid true weighing data output in step S3, it immediately triggers the Bluetooth channel scanning program. All network terminals synchronously scan the available Bluetooth channels in the current environment, covering all 40 dedicated channels in the 2.4GHz band. The system establishes channels and collects three core parameters in real time: interference intensity, signal-to-noise ratio, and channel occupancy rate. Based on the scanning results, the system assigns a unique dedicated transmission channel to each embedded weighing terminal according to the selection principle of lowest interference intensity, highest signal-to-noise ratio, and lowest occupancy rate, preventing channel overlap and resource contention among multiple devices. After channel allocation, the terminal and the host computer initiate a two-way link handshake procedure. The terminal sends a handshake signal containing the device's unique identification code, dedicated channel number, and transmission parameters to the host computer. After receiving the signal, the host computer returns a matching confirmation command, completing the permanent binding of the dedicated transmission link. During the link binding process, the system simultaneously locks the link transmission key and communication protocol parameters, prohibiting unauthorized devices from accessing the dedicated link and ensuring the uniqueness and security of data transmission. Simultaneously, the system monitors the link signal strength in real time and records the initial link transmission baseline parameters, providing channel-based data for subsequent transmission loss quantification calculations. The core working principle of this step is to replace the traditional fixed channel configuration mode with a full-domain channel scanning and dynamic dedicated addressing mechanism. This avoids channel conflicts, congestion, and crosstalk issues in multi-terminal Bluetooth transmission at the hardware link level, constructing a one-to-one independent transmission link to ensure the stability and independence of subsequent weighing data transmission. After this step is completed, all terminal Bluetooth transmission links are in a ready state and can immediately execute encrypted data transmission operations.

6. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 5, characterized in that, Step S5 is described in detail as follows: This step, based on the dedicated Bluetooth transmission link binding completed in step S4, performs time-series feature encoding and encryption encapsulation on the weighing truth data reconstructed in step S3 to ensure the integrity, uniqueness, and security of the transmitted data, and to avoid tampering, loss, or corruption during data transmission. The specific operation process and working principle are as follows: First, the system extracts four core types of information: the noise-reduced weighing truth data, the bound time-series timestamp, the device's unique identification code, and the dedicated channel number, to construct the core data packet; then, an adaptive time-series coding algorithm is used... Based on the continuous nature of the data acquisition time sequence, the data packets are segmented and encoded. Every 100 sets of time-series continuous data generate an independent encoded frame. The encoded frame contains four types of identification information: frame header identification code, data time sequence interval, data check code, and device identification code, ensuring that each segment of data can be accurately traced and its time sequence is verifiable. After encoding, the system uses the AES-128 symmetric encryption algorithm to fully encrypt the encoded data packets. The encryption key is dynamically generated by the device hardware parameters and time sequence parameters. Each set of transmitted data packets corresponds to a unique encryption key, eliminating the risk of data leakage caused by fixed keys. During the encryption and encapsulation process, the system synchronously generates a data packet verification hash value, embedding the hash value at the end of the data packet as the core basis for subsequent data reception verification. The encapsulated data packet is temporarily stored in the terminal transmission buffer and queued for transmission according to time priority, with no out-of-order transmission. The core working principle of this step is to achieve accurate identification and time-sequence solidification of data through feature encoding, and to achieve secure protection of transmitted data through dynamic encryption and encapsulation. This solves the problems of data disorder, tampering, and difficulty in tracing caused by the lack of encoding and encryption in traditional Bluetooth data transmission, and provides a standardized data packet carrier for subsequent high-precision transmission loss correction. The data packet encrypted and encapsulated in this step provides a standardized transmission unit for subsequent transmission loss quantification calculation and data correction.

7. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 6, characterized in that, Step S6 is described in detail as follows: This step takes the standardized data packet encrypted and encapsulated in step S5 and the Bluetooth channel basic parameters in step S4 as prerequisites. It uses an original calculus statistical model to dynamically quantify the Bluetooth transmission loss under different operating conditions and links, thereby achieving accurate calculation of transmission deviation and replacing the traditional fixed loss compensation mode. This step incorporates a second completely original mathematical statistical formula, which has a brand-new mathematical structure and technological contribution. The specific operation process and working principle are as follows: First, the real-time transmission parameters of the dedicated Bluetooth link are extracted, including the link transmission distance, signal attenuation coefficient, channel interference value, data transmission rate, and environmental obstruction coefficient, to construct a dynamic variable system of transmission loss. Through the original partial derivative + definite integral coupled mathematical formula, the nonlinear and dynamic loss of the transmission process is fully quantified and calculated to accurately obtain the real-time transmission loss value of each dedicated link. ； Formula 2 Symbol Definitions: Quantification of real-time transmission loss in Bluetooth links; is a natural constant with a value of 2.7182818285; The critical value for the chi-square distribution is 3.8414588207. This refers to the duration of a single data packet transmission. The fundamental function of transmission loss is determined by the transmission distance variable. With channel interference variables Together constitute; It is the first-order partial derivative of transmission loss with respect to transmission distance, which characterizes the rate of change of loss due to changes in distance; For transmission distance differential unit; Pi; It is the Euler-Macheroni constant; This formula integrates first-order partial derivatives and definite integrals in calculus, coupling four core constants: the natural constant, the chi-square distribution critical value, pi, and the Euler-Marcheroni constant. It captures the instantaneous loss changes over the transmission distance through partial derivatives and completes the cumulative loss calculation for the entire transmission time through definite integrals. The constant coupling structure is completely original and accurately adapts to the nonlinear loss characteristics of Bluetooth wireless transmission. The real-time loss value calculated by the formula has no fixed empirical parameters and is fully adapted to real-time link conditions. After the calculation is completed, the system performs pre-compensation correction on the encrypted data packet based on the loss quantification value to offset the signal attenuation and data offset during transmission, ensuring that the data received by the host computer is completely consistent with the original true data of the terminal. The core working principle of this step is to dynamically quantify the nonlinear transmission loss through a calculus model, which solves the technical defect that traditional fixed loss parameters cannot adapt to dynamic transmission conditions, realizes accurate and differentiated loss compensation for each Bluetooth link, and ensures the synchronization and accuracy of data transmission between multiple terminals. The standardized data packet after correction in this step provides accurate transmission data for subsequent cloud data aggregation and integration.

8. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 7, characterized in that, The specific description of step S7 is as follows: This step is based on the accurate data packet that was successfully transmitted after loss correction in step S6. It completes the cloud aggregation, decryption, time sequence alignment and integration classification of massive weighing data from multiple terminals, providing a complete and unified dataset for subsequent scheduling weight assignment. The specific operation process and working principle are as follows: After the host computer Bluetooth module receives the encrypted data packets transmitted by all terminals, it first completes the full-domain data decryption through the preset dynamic key to restore the original weighing true value data, time sequence information, equipment information and working condition information. The system then retrieves the unified timing reference calibrated in step S1 and performs timing alignment processing on the scattered data from all terminals to eliminate timing deviations caused by transmission delays between different terminals. This aggregates the weighing data from multiple terminals at the same operating time into a unified timing node. After timing alignment, the system categorizes and integrates the data according to three dimensions: equipment number, operating area, and weighing condition, constructing a structured cloud dataset. This dataset contains five dimensions of information for each device: real-time weighing value, historical data trend, equipment operating condition, transmission loss parameters, and data deviation value. During the integration process, the system simultaneously performs data integrity verification, comparing the locally cached data on the terminal with the data received from the cloud. The system verifies data consistency, eliminates a small number of abnormal packet losses during transmission, and supplements missing time-series data to ensure the integrity and accuracy of the cloud dataset. Simultaneously, the system updates the data deviation ledger for each terminal in real time, recording the long-term data fluctuation characteristics of each device to provide data support for subsequent weight assignment. The core working principle of this step is to solve the problems of scattered data, disordered time sequences, and inconsistent dimensions across multiple terminals by aggregating data across the entire domain and accurately aligning it with time sequences. This constructs a standardized, structured, and quantifiable full-domain operation dataset, enabling full-domain visualization of the operation status of multiple devices. The structured cloud dataset generated in this step provides the sole data basis for subsequent device scheduling weight assignment.

9. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 8, characterized in that, Step S8 is described in detail as follows: This step takes the structured cloud dataset integrated in step S7 as a prerequisite, completes the mapping of working condition parameters and intelligent scheduling weight assignment for each embedded weighing terminal, and provides quantitative weight parameters for scheduling priority modeling; the specific operation process and working principle are as follows: The system extracts five core working condition parameters from the cloud dataset: real-time load rate, data deviation value, device runtime, channel interference level, and job priority of each device, constructs a working condition parameter mapping matrix, and transforms the physical working condition status into quantifiable numerical parameters; a standardized quantitative range is set for each parameter. The higher the load rate, the larger the data deviation value, and the higher the channel interference level of the device, the higher the job urgency and the larger the basic weight value. The higher the reliability weight of the equipment that operates continuously and stably and has high data accuracy, the higher the reliability weight of the operation. The system adopts a multi-dimensional weighted fusion algorithm to couple and calculate five types of quantitative parameters to generate a real-time comprehensive scheduling weight value for each piece of equipment. The weight assignment process is dynamically updated throughout, with a full-domain weight refresh every 200ms, matching the dynamic changes in equipment operating conditions in real time and eliminating scheduling lag issues caused by fixed weights. The weight values ​​are uniformly set between 0 and 100. The larger the value, the higher the priority of the device scheduling, requiring priority in responding to system scheduling commands, priority in occupying channel resources, and priority in completing data uploads and job execution. After the values ​​are assigned, the system generates a global device weight ledger, which is synchronized to the scheduling core module in real time, providing accurate quantitative parameters for subsequent priority modeling. The core working principle of this step is to transform the physical operating status of the device into a quantifiable scheduling basis through digital mapping of operating parameters and dynamic weight assignment, solving the shortcomings of traditional scheduling that lacks quantitative standards and relies solely on fixed procedures, and realizing data-driven and intelligent scheduling decisions. The real-time scheduling weight values ​​generated in this step provide core parameter support for subsequent multi-device intelligent scheduling modeling.

10. The embedded weighing data Bluetooth linkage intelligent scheduling and control method according to claim 9, characterized in that, The specific description of step S9 is as follows: This step takes the real-time scheduling weight value output in step S8 and the equipment operating data in step S7 as the prerequisite, and completes the dynamic modeling of the scheduling priority of the entire domain equipment through the original triple integral calculus statistical model to realize intelligent differentiated scheduling of multiple devices. This step incorporates a third completely original mathematical statistical formula, which has a novel structure, no overlap with existing technologies, and has substantial technological innovation. The specific operation process and working principle are as follows: The system extracts three types of three-dimensional quantitative parameters of scheduling weight, timing deviation, and operating condition interference intensity of all terminals, constructs a three-dimensional model of the scheduling parameters of the entire domain, and completes the full-domain coupling calculation of the three-dimensional parameters through the original triple integral mathematical formula to generate the real-time dynamic scheduling priority coefficient of each device. ； Formula 3 Symbol Definitions: This is the real-time scheduling priority coefficient for the equipment; the larger the value, the higher the priority. It is the golden ratio; It is a natural constant; The value is 1.6448536269, which is the 95th percentile of the standard normal distribution. The integral space is a three-dimensional space of weights, time series, and operating conditions. Real-time scheduling weight variables for equipment; For equipment timing deviation variables; For equipment operating condition disturbance intensity variables; For the differential unit of the corresponding dimension; It is the Euler-Macheroni constant; Pi; This formula employs triple integrals to perform global coupling calculations of three-dimensional scheduling parameters, integrating five constants: the golden ratio, natural constants, standard normal distribution quantiles, Euler-Marschroni constant, and pi. The three-dimensional differential coupling structure and constant fusion method are entirely original, with no publicly available formulas matching it. This formula can accurately quantify the comprehensive operational needs of equipment and dynamically distinguish between high-priority and low-priority equipment. After calculation, the system sorts all equipment according to priority coefficients from high to low, generating a real-time dynamic scheduling sequence list, replacing the traditional fixed-time scheduling sequence. The core working principle of this step is to achieve accurate dynamic quantification of scheduling priorities through three-dimensional global calculus modeling and the integration of multi-dimensional core scheduling parameters, solving the core defects of traditional fixed-time scheduling that cannot adapt to dynamic working conditions and suffer from unreasonable resource allocation. The dynamic scheduling sequence generated in this step provides the core decision-making basis for subsequent scheduling command issuance and execution. Step S10 is described in detail as follows: This step uses the dynamic scheduling priority sequence generated in step S9 as the sole prerequisite to complete the differentiated issuance of hierarchical scheduling instructions, equipment operation execution, and full-process closed-loop verification, thereby realizing the implementation of intelligent scheduling. The specific operation process and working principle are as follows: The system divides the scheduling into three levels according to the dynamic scheduling sequence. Level 1 is for high-priority emergency scheduling equipment, which prioritizes the issuance of data upload, parameter correction, and operation start / stop instructions and exclusively occupies Bluetooth channel resources. Level 2 is for regular operation equipment, which executes operation tasks sequentially according to the sequence. Level 3 is for low-priority standby equipment, which remains in a standby ready state and does not occupy channel and system resources. The scheduling instructions adopt a one-to-one dedicated link issuance mode and are accurately transmitted through the dedicated Bluetooth channel bound in step S4, with no instruction crosstalk and no instruction loss. After receiving a dedicated scheduling instruction, the terminal immediately executes the corresponding job operation and synchronously returns an execution status feedback signal to the host computer. The system collects the instruction execution progress, data upload status, and job operation parameters of all devices in real time, compares and verifies them with preset standard execution parameters, and determines the validity of instruction execution. If the device accurately completes the instruction operation, it records the execution log and enters standby to wait for the next round of scheduling. If the device's execution deviation exceeds the standard threshold, the system immediately issues a secondary correction instruction, forcing the device to complete parameter correction and job completion. The core working principle of this step is to achieve accurate implementation of system scheduling instructions through hierarchical differentiated scheduling and closed-loop verification mechanisms, ensuring priority response for high-demand devices and orderly operation of all devices, and eliminating problems such as disordered scheduling, invalid instructions, and execution deviations. This step completes a single closed-loop operation of the entire domain scheduling, providing execution data for subsequent iterations and updates. Step S11 is described in detail as follows: This step uses the single-cycle scheduling closed-loop execution data completed in step S10 as a prerequisite to realize full-process data iterative storage and adaptive system parameter updates, and to build a continuously optimized intelligent scheduling system. The specific operation process and working principle are as follows: After a single scheduling job is completed, the system collects all dimensions of data, including hardware parameters, collected data, noise reduction parameters, transmission loss values, scheduling weights, priority coefficients, and execution results, and stores them in the cloud database to form an iterative training dataset. Through data comparison and analysis, the system identifies the parameter deviation patterns, noise distribution patterns, transmission loss change patterns, and scheduling adaptation patterns of the job, and extracts effective optimization features. Based on the optimization features, the system adaptively updates the hardware baseline compensation parameters, noise filtering thresholds, transmission loss correction coefficients, scheduling weight ratio parameters, and priority modeling basic parameters. All parameter updates are based on the real data of the job, without manual intervention or fixed parameter fixation. After the parameters are updated, the system locks the new baseline parameters and applies them to the next round of global scheduling, achieving continuous iterative optimization of system performance. Simultaneously, the system stores historical data from the entire process for an extended period, building a big data analysis model to support long-term operational condition adaptation and scheduling accuracy optimization. The core working principle of this step is to overcome the shortcomings of traditional systems, such as fixed parameters and inability to adapt to long-term operational condition changes, through iterative data updates and adaptive parameter updates, achieving intelligent upgrades with self-optimization and self-correction. After this step is completed, the system returns to its initial ready state and initiates the next round of closed-loop scheduling, enabling continuous intelligent operation around the clock.

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