An intelligent suspension conveying system for conveyor belt production

Abstract

The present application relates to the technical field of process control, in particular to an intelligent suspension conveying system for conveyor belt production, which comprises a pitch calculation module, a pitch analysis module, a density calculation module, an instruction generation module and a voltage optimization module. In the present application, the pitch sequence is obtained by collecting the timestamp of the suspension carrier trigger and analyzing the time difference, the mutation aggregation position is extracted by combining differential analysis to generate the density parameter, the dynamic conveying instruction is constructed according to the turnout switching level and control voltage extracted from the pitch distribution, the adaptive operation is realized by adjusting the motor drive according to the reverse compensation of the voltage deviation coefficient, the material accumulation and transmission empty gear phenomenon at specific nodes are accurately relieved, the physical impact caused by mechanical stop is effectively eliminated, the excessive wear and tear of parts and the probability of track jamming are greatly reduced, and the smoothness of the production line operation is comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of process control technology, and in particular to an intelligent overhead conveyor system for conveyor belt production. Background Technology

[0002] The field of process control technology involves monitoring, regulating, and coordinating various technological processes in industrial production. Its core aspects include controlling production cycle time, material flow paths, equipment operating status, and process connection relationships. By setting operating sequences, triggering conditions, and implementing logic, it achieves orderly connection and continuous operation between multiple workstations in the production line. It typically covers status acquisition based on sensor detection, sequential execution based on logical rules, and start / stop and operation path control of drive devices, thus forming a holistic control system for material delivery and process connection in discrete manufacturing processes.

[0003] Among them, the traditional intelligent overhead conveyor system used for conveyor belt production refers to the system in which, during the belt manufacturing process, the semi-finished belt to be processed is suspended sequentially on the conveyor path by setting up suspension rails, traction chains and hanging carriers. Limit switches detect the position of the carriers, and the drive motors are controlled to start and stop in a predetermined sequence by a preset relay control circuit or programmable controller, so that the carriers move between multiple processing stations along a fixed track. At critical nodes, mechanical stop devices or pneumatic push rods are used to pause and release the conveyor, so as to complete the conveying and connection between multiple processes in the belt production process.

[0004] Traditional conveyor overhead conveyor systems rely heavily on limit switches and preset control logic to start, stop, and release at nodes in a predetermined sequence. When dealing with fluctuations in production cycle, relying on hardware stop devices makes it difficult to implement dynamic adaptive adjustment. Triggering with a single position signal cannot effectively obtain the real-time distribution spacing of the overhead carriers and the overall congestion status on the line. Long-term adherence to rigid instructions for workstation connection can easily lead to abnormal accumulation of materials or the formation of transmission gaps at some processing nodes. Furthermore, frequent use of mechanical stop control can easily aggravate component wear and even cause track jamming and blockage, seriously affecting the continuous and stable operation efficiency of the entire conveyor belt production line. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides an intelligent overhead conveying system for conveyor belt production. The technical solution is as follows:

[0006] On the one hand, an intelligent overhead conveyor system for conveyor belt production is provided, the system comprising:

[0007] The spacing calculation module collects the trigger timestamp of the suspended vehicle at the position detection point on the conveyor track and analyzes the time difference between adjacent items. It calculates the spacing value by combining it with the preset running speed coefficient, generates an initial spacing sequence, and transmits it to the spacing analysis module.

[0008] The spacing analysis module performs standard deviation and mean analysis on the initial spacing sequence to obtain the spacing baseline range. At the same time, it extracts adjacent spacings and performs difference analysis. It determines the abrupt change position based on the difference results and combines them with preset start and end state identifiers to generate a set of clustering zone positions and transmits them to the density calculation module.

[0009] The density calculation module collects the correlation spacing of the cluster location set and filters the interval extreme values ​​with the set upper and lower limits. The interval extreme values ​​and correlation spacing are input into the Sigmoid function for compression mapping and arithmetic mean calculation to generate folded density parameters and pass them to the instruction generation module.

[0010] The instruction generation module classifies and compares the initial spacing sequence with the spacing reference range to analyze the dominant status code, extracts the turnout switching level by combining the set status dictionary set, extracts the motor control voltage by comparing the folding density parameter with the preset critical danger threshold, and packages the data by combining the turnout switching level to generate a transmission instruction set.

[0011] The voltage optimization module, based on the transmission instruction set, calculates the voltage deviation coefficient between the motor control voltage and the set operating limit value, adjusts the motor control voltage in reverse compensation according to the voltage deviation coefficient, and reconstructs the data in combination with the turnout switching level to build an optimized transmission coordination instruction.

[0012] As a further embodiment of the present invention, the initial spacing sequence includes a time difference sequence, a speed conversion spacing, and a sequence index identifier; the spacing reference range includes an upper bound of the mean interval, a lower bound of the mean interval, and a standard deviation fluctuation range; the clustering zone location set includes a mutation start position, a mutation end position, and a continuous clustering interval identifier; the folding density parameters include a compression mapping density, an interval extreme value weight, and an average fusion density factor; the transport instruction set includes a dominant status code, a turnout switching level, and a motor control voltage instruction; and the optimized transport coordination instruction includes a compensation voltage, a coordination control identifier, and a reconstructed data frame structure.

[0013] As a further aspect of the present invention, the spacing calculation module includes:

[0014] The time analysis submodule collects the trigger timestamps of the detection points of the suspended vehicle on the conveyor track, writes the corresponding time sequence number of the trigger signal of the detection point into the buffer queue, sorts the timestamps in the buffer queue according to the arrival order, removes duplicate time markers according to the timestamp record sequence, and generates a trigger timestamp sequence.

[0015] The time difference generation submodule calls adjacent time items to calculate the difference based on the trigger timestamp sequence, subtracts the previous timestamp from the next timestamp to form a time difference sequence, and reads the preset running speed coefficient item by item in the time difference sequence to perform numerical product operation to generate a set of interval values.

[0016] The spacing sequence submodule extracts the timestamp indices corresponding to multiple spacing values ​​based on the spacing value set, pairs and combines the spacing values ​​with the timestamps and writes them into a sorting queue, judges the order of the timestamps in the sorting queue and sorts them in ascending order to generate the initial spacing sequence.

[0017] As a further aspect of the present invention, the spacing analysis module includes:

[0018] The spacing statistics submodule, based on the initial spacing sequence, accumulates multiple spacing values ​​and records the number of elements. It analyzes the mean based on the sum and the number of elements, and simultaneously squares and accumulates the differences between multiple spacings and the mean, calculates the dispersion based on the number of elements, and combines the mean with the upper and lower superposition to generate the spacing benchmark interval.

[0019] The difference construction submodule calls the initial spacing sequence based on the spacing benchmark interval, extracts adjacent two spacing values ​​according to the sequence index order, performs item-by-item subtraction operation, records each pair of adjacent spacing differences and arranges them according to the original index position to obtain the spacing difference sequence.

[0020] The mutation location submodule reads the difference elements one by one according to the interval difference sequence and compares them with the set differential mutation threshold. The positions that exceed the differential mutation threshold are marked as mutation point coordinates. At the same time, the preset start state identifier and end state identifier are extracted and combined in coordinate order to obtain the set of aggregation zone positions.

[0021] As a further aspect of the present invention, the differential mutation threshold is obtained by acquiring the benchmark spacing test sequence under the standard operating state of the conveyor and subtracting adjacent terms to obtain a benchmark differential sample set, statistically analyzing the mean and dispersion parameters of all differential values ​​in the benchmark differential sample set, multiplying the dispersion parameters with a preset distribution coefficient to generate a fluctuation tolerance limit, and then summing the fluctuation tolerance limit with the mean to determine the threshold.

[0022] As a further aspect of the present invention, the density calculation module includes:

[0023] The spacing filtering submodule collects the associated spacing of the cluster location set and compares it with the set upper and lower limits of the boundary. Based on the multiple associated spacing values ​​and the upper and lower limits of the boundary, it performs interval discrimination item by item and removes out-of-bounds items, extracts the maximum and minimum values, and generates the interval extreme value set.

[0024] The mapping compression submodule constructs a dual-input data pair based on the interval extreme value set and the associated spacing numerical sequence. It uses the interval extreme values ​​as boundary reference values ​​and performs numerical normalization on each associated spacing. Then, it substitutes the normalization result into the compression expression constructed by the Sigmoid function to map each item and generate a feature parameter sequence.

[0025] The mean generation submodule performs item-by-item accumulation based on the feature parameter sequence and records the total value. At the same time, it counts the number of parameters in the sequence as the counting benchmark value, and performs a ratio operation between the total value and the counting benchmark value to generate the folding density parameter.

[0026] As a further aspect of the present invention, the instruction generation module includes:

[0027] The hierarchical construction submodule classifies and compares the initial spacing sequence with the spacing benchmark interval to construct a hierarchical distribution set, obtains the multi-element values ​​of the sequence and reads the upper and lower bounds of the mean interval of the spacing benchmark range, performs interval assignment judgment on the element values ​​and the corresponding interval boundaries and extracts extreme values ​​simultaneously to generate a hierarchical distribution frequency set.

[0028] The status coding submodule performs element frequency statistics and extreme value extraction based on the hierarchical distribution frequency set, calls and sorts the frequency data corresponding to multiple levels, selects the hierarchical index corresponding to the maximum frequency value and extracts extreme value data synchronously, combines them into a dominant status code and matches it with a set status dictionary to obtain the turnout switching level.

[0029] The level encapsulation submodule compares the folding density parameter with a preset critical danger threshold to determine the voltage mapping range and extracts the motor control voltage based on the range mapping attribute. It then calls the turnout switching level and the motor control voltage to perform data bit splicing and identification assignment operations to generate a transport instruction set.

[0030] As a further aspect of the present invention, the critical danger threshold is determined by acquiring the vehicle distribution density data record of the conveyor under extreme load conditions, extracting the minimum safe stopping distance value, calculating the extreme bearing density base by the ratio of the preset track section length to the minimum safe stopping distance value, and performing a product operation in combination with the preset safety derating multiplier.

[0031] As a further aspect of the present invention, the voltage optimization module includes:

[0032] The deviation coefficient submodule, based on the transmission instruction set, reads the motor control voltage and calculates the difference between the set operating limit value and extracts the voltage offset sequence. It then calculates the normalized ratio between the voltage offset sequence and the set voltage reference amplitude and performs interval mapping to unify the dimensions, thereby generating the voltage deviation coefficient.

[0033] The compensation voltage submodule, based on the voltage deviation coefficient, calls the original sequence of motor control voltage and performs inverse proportional operation, multiplies the voltage deviation coefficient with the motor control voltage point by point to calculate the compensation voltage component, and simultaneously performs superposition operation with the motor control voltage and performs amplitude limiting to obtain the target motor voltage.

[0034] The instruction reconstruction submodule, based on the target motor voltage, obtains the turnout switching level and performs synchronization alignment processing, splices the target motor voltage sequence and the turnout switching level, analyzes the multi-dimensional mapping relationship, rearranges the multi-dimensional mapping data structure and reorganizes the encoding format, and generates optimized transport coordination instructions.

[0035] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0036] By collecting the trigger timestamps of the suspended vehicle and analyzing the time difference to obtain the spacing sequence, and combining differential analysis to extract the abrupt clustering positions to generate density parameters, dynamic conveying instructions are constructed by extracting the turnout switching level and control voltage based on the spacing distribution. The motor drive is adjusted in reverse compensation based on the voltage deviation coefficient to achieve adaptive operation, accurately alleviate the material accumulation and transmission gap phenomenon at specific nodes, effectively eliminate the physical impact caused by mechanical stops, significantly reduce excessive wear of components and the probability of track jamming, and comprehensively improve the smoothness of production line operation. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the system of the present invention;

[0039] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0040] Figure 3 This is a flowchart of the spacing calculation module in this invention;

[0041] Figure 4 This is a flowchart of the spacing analysis module in this invention;

[0042] Figure 5 This is a flowchart of the density calculation module in this invention;

[0043] Figure 6 This is a flowchart of the instruction generation module in this invention;

[0044] Figure 7This is a flowchart of the voltage optimization module in this invention. Detailed Implementation

[0045] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0046] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0047] This invention provides an intelligent overhead conveyor system for conveyor belt production, such as... Figure 1-2 The diagram shows a schematic of an intelligent overhead conveyor system for conveyor belt production. The system includes:

[0048] The spacing calculation module collects the trigger timestamp of the suspended vehicle at the position detection point on the conveyor track and analyzes the time difference between adjacent items. It calculates the spacing value by combining it with the preset running speed coefficient, generates an initial spacing sequence, and transmits it to the spacing analysis module.

[0049] The spacing analysis module performs standard deviation and mean analysis on the initial spacing sequence to obtain the spacing baseline range. At the same time, it extracts adjacent spacings and performs difference analysis. It determines the abrupt change position based on the difference results and combines them with preset start and end status indicators to generate a set of clustering zone positions and transmits them to the density calculation module.

[0050] The density calculation module collects the correlation spacing of the cluster location set and filters the extreme values ​​of the interval with the set upper and lower limits. The extreme values ​​of the interval and the correlation spacing are input into the Sigmoid function for compression mapping and arithmetic mean calculation to generate folded density parameters and pass them to the instruction generation module.

[0051] The instruction generation module classifies and compares the initial spacing sequence with the spacing reference range to analyze the dominant status code, extracts the turnout switching level by combining the set status dictionary set, extracts the motor control voltage by comparing the folding density parameter with the preset critical danger threshold, and packages the data by combining the turnout switching level to generate a transmission instruction set.

[0052] The voltage optimization module, based on the transmission instruction set, calculates the voltage deviation coefficient between the motor control voltage and the set operating limit value, adjusts the motor control voltage in reverse compensation according to the voltage deviation coefficient, and reconstructs the data by combining the turnout switching level to build optimized transmission coordination instructions.

[0053] The initial spacing sequence includes the time difference sequence, speed conversion spacing, and sequence index identifier. The spacing reference range includes the upper bound of the mean interval, the lower bound of the mean interval, and the standard deviation fluctuation range. The clustering zone location set includes the mutation start position, mutation end position, and continuous clustering interval identifier. The folding density parameters include the compression mapping density, interval extreme value weight, and average fusion density factor. The transport instruction set includes the dominant status code, turnout switching level, and motor control voltage instruction. The optimized transport coordination instruction includes the compensation voltage, coordination control identifier, and reconstructed data frame structure.

[0054] Specifically, such as Figure 2 , 3 As shown, the spacing calculation module includes:

[0055] The time analysis submodule collects the trigger timestamps of the detection points of the suspended vehicle on the conveyor track, writes the corresponding time sequence number of the trigger signal of the detection point into the buffer queue, sorts the timestamps in the buffer queue according to the arrival order, removes duplicate time markers according to the timestamp record sequence, and generates a trigger timestamp sequence.

[0056] Photoelectric proximity switches deployed along the conveyor track collect the trigger timestamps when the suspended vehicle passes by, and write the timing numbers corresponding to the trigger signals at the detection points into a buffer queue component in memory. The signal acquisition component reads the level transitions of the photoelectric proximity switches at a sampling frequency of 1000 times per second. When a transition from low to high level is detected, the internal timing unit records the current microsecond-level timestamp. The buffer queue component receives continuously incoming timestamp data, and the data is written to the controller, which stores it as a contiguous array structure according to the order in which the data arrives at the memory address. The sorting unit reads the current time value and the previous time value from the timestamps in the buffer queue, performing a value comparison operation. If the previous time value is greater than the current time value, a data swap operation is performed within the memory address until all time values ​​are arranged in ascending order. The deduplication unit reads two adjacent timestamp values, performs a difference calculation by subtracting the previous timestamp from the subsequent timestamp, and compares the calculated difference with a preset time jitter threshold. The time jitter threshold setting relies on the hardware anti-jitter parameters of the photoelectric proximity switch. The test calibration component, under no-load conditions, allows the vehicle to pass the detection point at a constant speed 100 times, collecting the time interval between adjacent false trigger signals. The maximum value of all false trigger intervals is taken, and a 50-microsecond redundancy is added as the time jitter threshold. In the no-load test, the maximum false trigger interval is 150 microseconds; with the 50-microsecond redundancy, the time jitter threshold is set to 200 microseconds. The deduplication unit compares the difference between adjacent timestamps with 200 microseconds. If the difference is less than 200 microseconds, the discard component erases the subsequent timestamp from memory, retaining the previous timestamp as a valid record. The cache queue is configured with three consecutive timestamps of 10500 microseconds, 10650 microseconds, and 15000 microseconds. The difference between the first and second timestamps is 150 microseconds, less than 200 microseconds, so the 10650 microsecond data is discarded. Then, the difference between the first and third timestamps is calculated to be 4500 microseconds, greater than 200 microseconds, so the 15000 microsecond data is retained. Finally, a trigger timestamp sequence containing only 10500 microseconds and 15000 microseconds is generated.

[0057] The time difference generation submodule calls adjacent time items based on the trigger timestamp sequence to calculate the difference, subtracts the previous timestamp from the next timestamp to form a time difference sequence, and reads the preset running speed coefficient item by item in the time difference sequence to perform numerical product operation to generate a set of interval values.

[0058] The difference calculation is performed based on the trigger timestamp sequence, calling adjacent time items. The difference calculation component reads data bit by bit from the memory starting address of the trigger timestamp sequence, extracts the next timestamp value corresponding to the current index and the previous timestamp value corresponding to the previous index, performs a subtraction operation (subtracting the previous timestamp from the next timestamp), and obtains the difference as a single time difference data. All calculated time difference data are stored sequentially in the time difference sequence component. Combining the previously retained 10500 microsecond and 15000 microsecond trigger timestamps, the difference calculation component extracts 15000 microseconds as the next timestamp and 10500 microseconds as the previous timestamp, subtracting them to obtain a time difference value of 4500 microseconds. The product calculation unit reads the time difference sequence item by item and retrieves the preset operating speed coefficient from the parameter storage unit for numerical product calculation. The specific setting of the preset operating speed coefficient refers to the rated speed of the conveyor drive motor and the transmission ratio of the reducer. The speed calibration component obtains the actual operating speed of the motor, divides it by the mechanical transmission reduction ratio, and then multiplies it by the pitch circle circumference of the drive sprocket to obtain the base linear velocity. A slip compensation constant is added based on the belt load weight. The base linear velocity and the slip compensation constant are then added to obtain the preset operating speed coefficient. The actual operating speed of the motor is set to 1500 rpm, the reduction ratio to 30, the drive sprocket circumference to 0.4 m, the base linear velocity to 0.33 m / s (0.00000033 m / microsecond), and the slip compensation constant to 0.00000002 m / microsecond. These sum to obtain the preset operating speed coefficient of 0.00000035 m / microsecond. The product calculation unit multiplies the previously obtained time difference of 4500 microseconds with the preset operating speed coefficient of 0.00000035 m / microsecond to calculate the spacing value as 0.001575 m (1.575 mm). All the spacing values ​​calculated sequentially are summarized and stored in the spacing value set component.

[0059] Table 1. Monitoring Table of Spacing Between Intelligent Suspended Conveyor Containers in Conveyor Belt Production

[0060]

[0061] Table 1 lists the time difference obtained at different detection nodes and the corresponding spacing data generated by the product operation.

[0062] The spacing sequence submodule extracts the timestamp indices corresponding to multiple spacing values ​​from the spacing value set, pairs and combines the spacing values ​​with the timestamps and writes them into a sorting queue, judges the order of the timestamps in the sorting queue and sorts them in ascending order to generate the initial spacing sequence.

[0063] The timestamp index corresponding to multiple spacing values ​​is extracted from the storage content within the spacing value set component. The index extraction unit reads the array index position of each spacing value in the spacing value set and locates the previous timestamp corresponding to that index position in the trigger timestamp sequence memory area as the corresponding timestamp index. The pairing and combination unit performs a memory association operation on the read spacing value data and the corresponding timestamp data, creating a composite structure containing time and distance attributes, and writes this composite structure to the tail of the sorting queue component one by one. The relationship judgment unit performs a timestamp order judgment operation on each composite structure within the sorting queue component, comparing the timestamp value of the current composite structure with the timestamp values ​​of adjacent composite structures. If the timestamp value of the current composite structure is greater than the timestamp value of the preceding composite structure, the sorting execution unit keeps its current memory position unchanged; if the timestamp value of the current composite structure is less than the timestamp value of the preceding composite structure, the sorting execution unit performs a position swap operation until all composite structures in the sorting queue component are strictly arranged in ascending order of timestamp values. Combining the previously obtained node 1 spacing value of 1.575 mm and its corresponding previous timestamp of 10500 microseconds, and the node 2 spacing value of 1.820 mm and its corresponding previous timestamp of 15000 microseconds, the pairing and combination unit pairs 1.575 mm with 10500 microseconds and 1.820 mm with 15000 microseconds. The relationship judgment unit compares 15000 microseconds with 10500 microseconds, determining that 15000 microseconds is greater than 10500 microseconds. The sorting execution unit places the composite structure containing 10500 microseconds at the beginning of the sequence and the composite structure containing 15000 microseconds at the end of the sequence. For the sorting process of large amounts of data, the scheduling component repeatedly executes the aforementioned position judgment and swapping operations. The sequence output unit stores all sorted composite structures in a fixed manner, generating the initial spacing sequence. The values ​​in the initial spacing sequence reflect the actual spatial distribution of the suspended vehicles at different times. These values ​​are then compared with the preset vehicle safety distance benchmark to deduce the relative density of the vehicles on the track.

[0064] Specifically, such as Figure 2 , 4 As shown, the spacing analysis module includes:

[0065] The spacing statistics submodule, based on the initial spacing sequence, accumulates multiple spacing values ​​and records the number of elements. It analyzes the mean based on the sum and the number of elements, and simultaneously squares and accumulates the differences between multiple spacings and the mean, calculates the dispersion based on the number of elements, and combines the mean with the upper and lower superposition to generate the spacing benchmark interval.

[0066] The system reads data from the initial interval sequence one by one from memory. The numerical accumulation unit performs a summation operation on the read interval values ​​to generate the total interval data. Simultaneously, the counting component records the number of interval elements based on the number of times the memory read pointer moves. The mean calculation unit obtains the total interval data and divides it by the number of interval elements to obtain the interval mean, which is then synchronously pushed into the shared memory address. The dispersion analysis unit extracts each interval value and subtracts the interval mean from it to obtain the interval deviation data. Then, it performs a product operation on each interval deviation data to obtain the deviation square data. The summation unit adds all the deviation square data to obtain the cumulative sum of squares, divides it by the number of interval elements to obtain the variance value, and performs a square root operation on the variance value to obtain the standard deviation value as the dispersion data. The interval generation unit obtains the interval mean and dispersion data, and performs addition and subtraction operations on the interval mean and dispersion data respectively to obtain the upper and lower limit values ​​of the benchmark, generating the interval benchmark interval. In the test scenario, five spacing values ​​were read: 1575 mm, 1820 mm, 1680 mm, 1750 mm, and 1525 mm. The value accumulation unit calculated the total spacing to be 8350 mm. The counting component recorded 5 spacing elements. The mean calculation unit divided 8350 mm by 5 to obtain the spacing mean of 1670 mm. The dispersion analysis unit calculated and squared the deviations of each value from 1670 mm, obtaining 9025 square millimeters, 22500 square millimeters, 100 square millimeters, 6400 square millimeters, and 21025 square millimeters respectively. The sum of the squares was 59050 square millimeters. Dividing by 5, the variance was 11810 square millimeters. The square root of the variance was 108.67 mm. The interval generation unit added to and subtracted 108.67 mm from 1670 mm to obtain the upper limit of the baseline value of 1778.67 mm and the lower limit of the baseline value of 1561.33 mm.

[0067] The difference construction submodule calls the initial spacing sequence based on the spacing benchmark interval, extracts adjacent two spacing values ​​according to the sequence index order, performs item-by-item subtraction operation, records the difference of each pair of adjacent spacing values ​​and arranges them according to the original index position to obtain the spacing difference sequence.

[0068] After confirming that all values ​​in the initial spacing sequence are within the spacing baseline range, adjacent spacing values ​​are extracted according to the sequence index order based on the memory address auto-increment mechanism. The difference calculation unit receives the extracted adjacent value data and subtracts the previous spacing value from the next spacing value at the current index position to obtain the relative distance change as the adjacent spacing difference. The index mapping component captures the original index number data corresponding to the previous spacing value involved in the calculation and performs a memory binding operation between the calculated adjacent spacing difference and the original index number data to generate difference node data with position attributes. The sequence integration unit stores all generated difference node data into the cache area in ascending order of the original index number data to obtain the spacing difference sequence. Substituting the five consecutive spacing values ​​of 1575 mm, 1820 mm, 1680 mm, 1750 mm, and 1525 mm in the previous example, corresponding to index numbers 1 to 5. The differential data reading component extracts 1820 mm corresponding to index 2 and 1575 mm corresponding to index 1. The difference calculation unit subtracts 1575 mm from 1820 mm to obtain an adjacent spacing difference of 245 mm, which is then bound to the original index number 1 by the index mapping component. Similarly, the differential data reading component extracts 1680 mm from index 3 and 1820 mm from index 2. The difference calculation unit performs the subtraction to obtain -140 mm, which is bound to index 2. It extracts 1750 mm and subtracts 1680 mm to obtain 70 mm, which is bound to index 3. It extracts 1525 mm and subtracts 1750 mm to obtain -225 mm, which is bound to index 4. The sequence integration unit sorts the data according to indices 1 to 4 and stores it in solid-state storage, completing the construction of the complete spacing difference sequence. The spacing difference sequence shows the increasing and decreasing trend of vehicle spacing in spatial distribution. This numerical result is pushed to subsequent components as input for mutation analysis.

[0069] The mutation location submodule reads the difference elements one by one according to the interval difference sequence and compares them with the set differential mutation threshold. The positions that exceed the differential mutation threshold are marked as mutation point coordinates. At the same time, the preset start state identifier and end state identifier are extracted and combined in coordinate order to obtain the set of aggregation zone locations.

[0070] The system reads the difference element data item by item from the spacing difference column and performs an absolute value comparison operation by calling the pre-stored differential mutation threshold. The threshold configuration unit obtains the maximum tensile deformation data and the maximum springback indentation data of the tool at the transmission sprocket under normal operating conditions, takes the maximum of the two absolute values, and multiplies it by the load compensation coefficient to obtain the differential mutation threshold. Under rated load, the measured maximum tensile deformation data is 120 mm, and the maximum springback indentation data is 140 mm. The threshold configuration unit takes 140 mm multiplied by a compensation coefficient of 1.5 and sets the differential mutation threshold to 210 mm. The differential comparison component extracts the elements 245 mm, -140 mm, 70 mm, and -225 mm from the spacing difference column in the previous example and compares their absolute values ​​with 210 mm respectively. The coordinate marking component detects that 245 mm is greater than 210 mm and marks its bound original index number 1 as the positive mutation point coordinate. It detects that the absolute value 225 mm is greater than 210 mm and marks its bound original index number 4 as the negative mutation point coordinate. The state quantization unit performs binarization on the non-numerical start and stop signals acquired from the sensors. The rule is set to assign a preset start state identifier value of 1 when a high-level sensor output is detected, and a preset end state identifier value of 0 when a low-level sensor output is detected. The data stitching component extracts the preset start state identifier value 1, combines it sequentially with the coordinates of consecutive positive and negative abrupt change points, and appends the preset end state identifier value 0 to the end of the sequence. Combining the aforementioned marking results, the data stitching component concatenates the start identifier value 1, coordinate index 1, coordinate index 4, and end identifier value 0 to obtain a sequence encoding of consecutive 1s and 4s of consecutive 0s for the clustering zone location set. The advantage of this operation logic is that by combining the quantized state identifier with the abrupt change point coordinates for stitching encoding, it provides standardized fault segment spatial location data support for subsequent control.

[0071] Specifically, such as Figure 2 , 5 As shown, the density calculation module includes:

[0072] The spacing filtering submodule collects the associated spacing of the cluster location set and compares it with the set upper and lower limits of the boundary. Based on the multiple associated spacing values ​​and the upper and lower limits of the boundary, it performs interval discrimination item by item and removes out-of-bounds items, extracts the maximum and minimum values, and generates the interval extreme value set.

[0073] The system receives coded data from the aggregation belt location set component and extracts multiple associated spacing values ​​within the aggregation belt index range from the initial spacing sequence storage area according to the memory mapping protocol. The threshold setting component dynamically configures the upper and lower limits of the boundary based on the physical limit dimensions of the conveyor belt links. It acquires the standard link length data, the maximum tensile tolerance data, and the minimum compressive tolerance data of the chain. The standard link length data is added to the maximum tensile tolerance data to obtain the upper limit, and the standard link length data is subtracted from the minimum compressive tolerance data to obtain the lower limit. The measured standard link length data is 1700 mm, the maximum tensile tolerance data is 200 mm, and the minimum compressive tolerance data is 200 mm. After addition and subtraction operations, the upper limit is set to 1900 mm, and the lower limit is set to 1500 mm. The comparison and elimination unit reads the associated spacing values ​​one by one and compares them with 1900 mm and 1500 mm. When a associated spacing value is detected to be greater than 1900 mm or less than 1500 mm, the discard component sets the memory pointer corresponding to the value to null and releases the storage space. If the value is within the valid range, the memory management component moves the corresponding value to the safe cache. Substituting the associated spacing values ​​of 1575 mm, 1820 mm, 1680 mm, and 1750 mm corresponding to the previously obtained clustering zone indices 1 to 4, the comparison and elimination unit determines that all four values ​​are between 1500 mm and 1900 mm and retains them all. The extreme value extraction component performs a traversal comparison operation on the four retained associated spacing values, extracts the first value as the initial maximum and minimum value, and then compares the subsequently read values ​​with the temporary maximum and temporary minimum values ​​and continuously updates and replaces them. Finally, the largest value, 1820 mm, is selected as the maximum value data, and the smallest value, 1575 mm, is selected as the minimum value data, and these two data are stored in the interval extreme value set component. The data within the interval extreme value set represents the actual amplitude boundary of the vehicle spacing within the clustered region, and this numerical result is pushed to the downstream conversion channel.

[0074] The mapping compression submodule constructs a dual-input data pair based on the interval extreme value set and the associated spacing numerical sequence. It uses the interval extreme values ​​as boundary reference values ​​and performs numerical normalization on each associated spacing. Then, it substitutes the normalization result into the compression expression constructed by the Sigmoid function to map each term and generate a feature parameter sequence.

[0075] The system reads the maximum and minimum values ​​from the interval extreme value set component and combines them with the associated spacing value item by item to generate multiple two-input data pairs. The normalization operation component performs a linear mapping transformation on each two-input data pair. It reads the current associated spacing value and subtracts the minimum value to obtain the offset difference. Simultaneously, it reads the maximum value and subtracts the minimum value to obtain the interval span value. Then, it divides the offset difference by the interval span value to obtain the normalized result. Using the previous example, with an associated spacing value of 1680 mm, a maximum value of 1820 mm, and a minimum value of 1575 mm, the normalization operation component subtracts 1575 mm from 1680 mm to obtain 105 mm, subtracts 1575 mm from 1820 mm to obtain 245 mm, and then divides 105 mm by 245 mm to obtain a normalized result of 0.43. Similarly, the normalized result for 1575 mm is 0, for 1820 mm it is 1, and for 1750 mm it is 0.71. The exponential mapping component uses the natural constant as the base, multiplies the normalized data by a preset compression constant, takes the opposite of the result as the exponent, performs a power operation to obtain an intermediate result, and then subtracts this intermediate result from 1 to obtain the feature parameter data. The preset compression constant is obtained by fitting multiple sets of spacing distribution experiments under different loads; the experimentally measured fitting constant for a load of 500 kg is 2. The exponential mapping component extracts the normalized data 0.43, multiplies it by 2 to obtain 0.86, takes the opposite of 0.86, performs a power operation on the natural constant to obtain 0.423, and subtracts 0.423 from 1 to obtain the feature parameter data 0.577. The remaining three terms are calculated sequentially to obtain the feature parameter data 0, 0.865, and 0.759, respectively. The sequence splicing unit stores all generated feature parameter data into the feature parameter sequence component according to the initial index order.

[0076] Table 2 Mapping and Transformation Table of Conveying Spacing Characteristic Parameters

[0077]

[0078] Table 2 lists the feature parameter data generated after normalization and exponential mapping compression of each correlation interval within the clustering zone.

[0079] The mean generation submodule accumulates each item based on the feature parameter sequence and records the total value. At the same time, it counts the number of parameters in the sequence as the counting benchmark value, and performs a ratio operation between the total value and the counting benchmark value to generate the folding density parameter.

[0080] The feature parameter sequence component reads feature parameter data item by item from the first memory address and pushes it into the arithmetic logic unit for continuous addition, generating a sum value and storing it in a specific register. The counter unit synchronously monitors the data accumulation component's read operations. Each time feature parameter data is retrieved from memory, the internal value of the counter unit increments by 1. After traversal, the current count value is latched as the counting base value. Substituting the four data items 0, 0.865, 0.577, and 0.759 from the aforementioned feature parameter sequence, the data accumulation component adds these four values, obtaining a sum of 2.201. The counter unit records the number of reads and obtains a counting base value of 4. The ratio operation unit retrieves the sum value from the register and the counting base value latched by the counter unit, performs a division operation by dividing the sum value by the counting base value, and obtains the quotient value as the folding density parameter data. The ratio operation unit divides the sum value 2.201 by the counting base value 4, calculating the folding density parameter data as 0.550. The output comparison unit compares the generated folding density parameter data with a preset congestion alarm threshold. The congestion alarm threshold is statistically determined based on the distribution of the mean values ​​of characteristic parameters from historical fault data when tracks jammed. The historical data acquisition component extracts characteristic parameters from the 3 seconds preceding 100 jamming faults, calculates their average value as 0.620, deducts a redundancy safety margin of 0.1, and sets the congestion alarm threshold to 0.520. When the output comparison unit detects that the current folding density parameter data (0.550) is greater than 0.520, it switches the control signal output port from low to high. The advantage of this calculation logic is that by using the ratio of sum to quantity, multi-dimensional spatial distance data is reduced to a single density index parameter, reducing the computational overhead of the underlying control chip. The folding density parameter data intuitively quantifies the overall compression degree of the vehicle within a local space; exceeding the limit of this value directly triggers subsequent safety intervention control procedures.

[0081] Specifically, such as Figure 2 , 6 As shown, the instruction generation module includes:

[0082] The hierarchical construction submodule classifies and compares the initial spacing sequence with the spacing benchmark interval to construct a hierarchical distribution set, obtains the multi-element values ​​of the sequence and reads the upper and lower bounds of the mean interval of the spacing benchmark range, performs interval assignment judgment on the element values ​​and the corresponding interval boundaries and extracts extreme values ​​simultaneously to generate a hierarchical distribution frequency set.

[0083] Multiple spacing element values ​​are extracted from the initial spacing sequence in memory, and the generated lower baseline value of 1561.33 mm and upper baseline value of 1778.67 mm are simultaneously invoked. The interval assignment judgment unit is configured with classification rules: ranges less than the lower baseline value are assigned to level 1; ranges between the lower and upper baseline values ​​are assigned to level 2; and ranges greater than the upper baseline value are assigned to level 3. The interval comparison component reads the aforementioned spacing values ​​of 1575 mm, 1820 mm, 1680 mm, 1750 mm, and 1525 mm for comparison. The interval comparison component determines that 1525 mm is less than 1561.33 mm and assigns it to level 1; determines that 1575 mm, 1680 mm, and 1750 mm are within the interval and assign them to level 2; and determines that 1820 mm is greater than 1778.67 mm and assigns it to level 3. The extreme value extraction component performs a traversal comparison operation on the data for each level. For level 1, 1525 mm is extracted as the extreme value. For level 2, the maximum value of 1750 mm and the minimum value of 1575 mm are compared and used as extreme values. For level 3, 1820 mm is extracted as the extreme value. The frequency accumulation unit counts the number of elements in each level, obtaining a frequency of 1 for level 1, 3 for level 2, and 1 for level 3. The level aggregation component performs a memory binding operation on each level number, its corresponding frequency data, and the extreme value data, storing them in the level distribution frequency set. The advantage of this operation logic is that by using hard boundary partitioning, continuous spacing data is transformed into discrete level features, reducing the load on the control chip performing floating-point operations.

[0084] The status coding submodule performs element frequency statistics and extreme value extraction based on the hierarchical distribution frequency set. It calls and sorts the frequency data corresponding to multiple levels, selects the hierarchical index corresponding to the maximum frequency value and extracts extreme value data synchronously, combines them into a dominant status code and matches it with the set status dictionary set to obtain the turnout switching level.

[0085] Frequency data from level 1 to level 3 are extracted from the hierarchical frequency distribution set and sent to the descending sorting unit for numerical comparison and sorting. For the extracted frequency data 1 for level 1, 3 for level 2, and 1 for level 3, the descending sorting unit compares the values ​​pairwise to determine that value 3 is the highest frequency in the current sequence. The index capture component locates the specific level containing the maximum frequency 3, extracts its associated level index number 2, and simultaneously retrieves the maximum extreme value data 1750 mm and the minimum extreme value data 1575 mm associated with level 2 from the data bus. The status code construction unit obtains the extracted level index number 2, maximum extreme value data 1750 mm, and minimum extreme value data 1575 mm, and performs string concatenation encapsulation operations according to the order of level index number, maximum extreme value data, and minimum extreme value data to generate a dominant status code containing two consecutive 1750 and 1575. The dictionary matching component reads a state dictionary set pre-stored in read-only memory. This dictionary set maps actions under different level state codes based on continuous test runs. In the test, a standard through voltage guide was set for a smooth passage condition where the dominant level index is 2 and the extreme value span is less than 200 mm. The dictionary matching component compares the generated dominant state code with the entries in the state dictionary set character by character. It successfully matches the standard action entry corresponding to index 2, which conforms to the smooth passage rule, and extracts the switch switching level data of 5 volts bound to this entry. The advantage of this operation logic is that by extracting the most frequent level features to construct the state word, a very small amount of abnormal fluctuation level data is filtered out, improving the robustness of the transmission status determination.

[0086] The level encapsulation submodule compares the folding density parameter with the preset critical danger threshold, determines the voltage mapping range, extracts the motor control voltage based on the range mapping attribute, calls the turnout switching level and the motor control voltage to perform data bit splicing and identification assignment operations, and generates a transport instruction set.

[0087] Based on the slope of the temperature rise curve of the conveyor motor under continuous high-load operation, a preset critical danger threshold is set. Test data shows that the motor exhibits rapid overheating when the folding density parameter exceeds 0.500. Therefore, the threshold configuration component fixes the preset critical danger threshold at 0.500. The interval comparison unit reads the previously calculated folding density parameter data of 0.550 and compares it with the preset critical danger threshold of 0.500. The status judgment component determines that the current folding density parameter data of 0.550 is greater than the danger threshold of 0.500, thus classifying the current operating condition into the high-voltage limit mapping range. The voltage extraction unit, based on the speed reduction protection mechanism built into the high-voltage limit mapping range, retrieves the reduced-voltage output motor control voltage data of 110 volts from the register of the power management controller. The data encapsulation component simultaneously acquires the previously calculated turnout switching level data of 5 volts and the motor control voltage data of 110 volts. The bitwise operation component converts the 5-volt turnout switching level data into 8-bit binary data and stores it in the high-order register segment, and converts the 110-volt motor control voltage data into 8-bit binary data and stores it in the low-order register segment. The device addressing unit extracts the unique device identifier number 9 as the message header and performs a continuous concatenation operation with the data within the high-order and low-order register segments. The instruction integration module encapsulates the device identifier number 9, the binary segment of the turnout switching level, and the binary segment of the motor control voltage into a standard data frame format, generating a complete transmission instruction set. The advantage of this operation logic is that it establishes a closed-loop control link directly from the information layer to the physical layer by using density parameter cross-boundary judgment to control the linked voltage output.

[0088] Specifically, such as Figure 2 , 7 As shown, the voltage optimization module includes:

[0089] The deviation coefficient submodule, based on the transmission instruction set, reads the motor control voltage and calculates the difference between the set operating limit value and extracts the voltage offset sequence. It then calculates the normalized ratio between the voltage offset sequence and the set voltage reference amplitude and performs interval mapping to unify the dimensions, thereby generating the voltage deviation coefficient.

[0090] The motor control voltage sequence is extracted and analyzed. Substituting this into the previously defined multi-cycle motor control voltage sequence, which includes 110 volts, 120 volts, and 115 volts, the limit value setting unit configures and sets the operating limit value. This setting is determined based on the insulation withstand parameters on the motor nameplate and laboratory locked-rotor overload extreme value test data. The test indicates that irreversible thermal damage occurs in the coil when the voltage reaches 150 volts; therefore, 150 volts is extracted as the constant setting for the operating limit value. The difference calculation component retrieves this set operating limit value and performs a step-by-step subtraction calculation with the motor control voltage sequence, subtracting 110 volts, 120 volts, and 115 volts from 150 volts, respectively, thus generating a voltage offset sequence containing 40 volts, 30 volts, and 35 volts. The reference configuration unit extracts the set voltage reference amplitude. This value is strictly defined by the international standard document on power supply network fluctuation tolerance and is manually pre-configured as a 200-volt constant parameter. The normalization calculation component divides each value within the voltage offset sequence with a set voltage reference amplitude, extracting 40 volts divided by 200 volts to obtain an initial ratio of 0.2, 30 volts divided by 200 volts to obtain an initial ratio of 0.15, and 35 volts divided by 200 volts to obtain an initial ratio of 0.175. The interval mapping component maps these dimensionless decimals to a unified coefficient scaling space to generate a voltage deviation coefficient sequence, corresponding to voltage deviation coefficients containing 0.2, 0.15, and 0.175. The advantage of this calculation logic is that by normalizing the difference by dividing it with a fixed reference, the absolute physical voltage is transformed into a deviation ratio reflecting the relative margin. The generated voltage deviation coefficient characterizes the remaining margin between the current driving voltage and the physical breakdown extreme value; this numerical result is directly fed into the subsequent compensation calculation module for processing.

[0091] The compensation voltage submodule, based on the voltage deviation coefficient, calls the original sequence of motor control voltage and performs inverse proportional calculation. It multiplies the voltage deviation coefficient with the motor control voltage point by point to calculate the compensation voltage component, and simultaneously performs superposition calculation with the motor control voltage and amplitude limiting to obtain the target motor voltage.

[0092] The system receives the generated voltage deviation coefficient sequence, which contains a set of coefficient values ​​of 0.2, 0.15, and 0.175. The sequence association unit synchronously retrieves the aforementioned original motor control voltage sequence, which contains the original drive data of 110 volts, 120 volts, and 115 volts. The inverse proportional calculation component multiplies the voltage deviation coefficients with the corresponding motor control voltages to calculate the compensation voltage components. Specifically, the calculation logic is as follows: 110 volts is extracted and multiplied by 0.2 to obtain the first compensation voltage component of 22 volts; 120 volts is extracted and multiplied by 0.15 to obtain the second compensation voltage component of 18 volts; and 115 volts is extracted and multiplied by 0.175 to obtain the third compensation voltage component of 20.125 volts. The voltage superposition component then adds the calculated compensation voltage components to the corresponding original sequence of motor control voltages, performing a combined calculation. Adding 110 volts to 22 volts yields an initial superposition voltage of 132 volts; adding 120 volts to 18 volts yields an initial superposition voltage of 138 volts; and adding 115 volts to 20.125 volts yields an initial superposition voltage of 135.125 volts. The amplitude limiting unit has a preset amplitude limiting threshold, set at 136 volts based on the principle of retaining approximately 10% redundancy for the physical limit of the motor's safe operating distance (150 volts). The comparison and truncation component compares each component of the initial superposition voltage with 136 volts. If the value is greater than this threshold, it is forcibly truncated to 136 volts; otherwise, the original value is retained. After comparing and screening the truncation components, 132 volts was retained, 138 volts was truncated to 136 volts, and 135.125 volts were retained, resulting in a target motor voltage sequence containing 132 volts, 136 volts, and 135.125 volts.

[0093] Table 3 Calculation Table for Drive Voltage Parameter Compensation

[0094]

[0095] Table 3 details the numerical evolution of the target voltage generated after proportional calculation and limiting of the motor control voltage for different cycles. The advantage of this operational logic is that it establishes a flexible adaptive compensation mechanism for motor power through dual control of coefficient multiplication and limiting. The target motor voltage clearly defines the compensated and corrected terminal drive potential energy, and this numerical result directly determines the base value of the terminal drive current output.

[0096] The instruction reconfiguration submodule, based on the target motor voltage, obtains the turnout switching level and performs synchronization alignment processing, splices the target motor voltage sequence with the turnout switching level and analyzes the multi-dimensional mapping relationship, rearranges the multi-dimensional mapping data structure and reorganizes the encoding format to generate optimized transport coordination instructions;

[0097] The generated target motor voltage sequence is extracted, which is a high-voltage drive data set containing 132 volts, 136 volts, and 135.125 volts. The state addressing component synchronously retrieves the previously generated turnout switching level and extracts the specific level value of 5 volts. The timestamp matching unit assigns the same timing number to the target motor voltage sequence and the turnout switching level based on the clock frequency to complete the data bit synchronization alignment. The data splicing component receives the synchronized and aligned values ​​and sequentially encapsulates the three target motor voltages with the turnout switching level in memory-level serial concatenation through the bus interface to generate a spliced ​​data block. The multi-dimensional mapping analysis unit constructs a two-dimensional mapping matrix table, filling the high-frequency drive signal target motor voltage into the high-order storage area of ​​the matrix and the low-order storage area of ​​the matrix representing the low-frequency state command turnout switching level. The structure rearrangement unit performs memory pointer swapping calculations on the multidimensional mapped data. Based on the high-priority parameter configuration requirements of the voltage drive signals, it prioritizes pushing 132 volts, 136 volts, and 135 and 125 volts into the head of the transmission queue, and pushes 5 volts into the tail of the transmission queue, attaching a cyclic redundancy check (CRC) code. The format reconstruction unit performs binary conversion and reconstruction of the rearranged queue content according to the Universal Asynchronous Receiver / Transmitter (UART) protocol specification, assigning start and end flag bits, and generating optimized transmission coordination instructions. The advantage of this operation logic is that by rearranging the multidimensional data structure, control signals of different frequencies and priorities are integrated into a unified data frame. The generated optimized transmission coordination instructions, as the highest-level execution signaling at the end, directly reach the underlying physical hardware. This instruction data directly controls external mechanical shunt and thrust actions.

[0098] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An intelligent overhead conveyor system for conveyor belt production, characterized in that, The system includes: The spacing calculation module collects the trigger timestamp of the suspended vehicle at the position detection point on the conveyor track and analyzes the time difference between adjacent items. It calculates the spacing value by combining it with the preset running speed coefficient, generates an initial spacing sequence, and transmits it to the spacing analysis module. The spacing analysis module performs standard deviation and mean analysis on the initial spacing sequence to obtain the spacing baseline range. At the same time, it extracts adjacent spacings and performs difference analysis. It determines the abrupt change position based on the difference results and combines them with preset start and end state identifiers to generate a set of clustering zone positions and transmits them to the density calculation module. The density calculation module collects the correlation spacing of the cluster location set and filters the interval extreme values ​​with the set upper and lower limits. The interval extreme values ​​and correlation spacing are input into the Sigmoid function for compression mapping and arithmetic mean calculation to generate folded density parameters and pass them to the instruction generation module. The instruction generation module classifies and compares the initial spacing sequence with the spacing reference range to analyze the dominant status code, extracts the turnout switching level by combining the set status dictionary set, extracts the motor control voltage by comparing the folding density parameter with the preset critical danger threshold, and packages the data with the turnout switching level to generate a transmission instruction set.

2. The intelligent overhead conveyor system for conveyor belt production according to claim 1, characterized in that, The initial spacing sequence includes a time difference sequence, a speed-converted spacing, and a sequence index identifier. The spacing reference range includes the upper bound of the mean interval, the lower bound of the mean interval, and the standard deviation fluctuation range. The clustering zone location set includes the mutation start position, the mutation end position, and the continuous clustering interval identifier. The folding density parameters include the compression mapping density, the interval extreme value weight, and the average fusion density factor. The transmission instruction set includes the dominant status code, the turnout switching level, and the motor control voltage instruction.

3. The intelligent overhead conveyor system for conveyor belt production according to claim 1, characterized in that, The spacing calculation module includes: The time analysis submodule collects the trigger timestamps of the detection points of the suspended vehicle on the conveyor track, writes the corresponding time sequence number of the trigger signal of the detection point into the buffer queue, sorts the timestamps in the buffer queue according to the arrival order, removes duplicate time markers according to the timestamp record sequence, and generates a trigger timestamp sequence. The time difference generation submodule calls adjacent time items to calculate the difference based on the trigger timestamp sequence, subtracts the previous timestamp from the next timestamp to form a time difference sequence, and reads the preset running speed coefficient item by item in the time difference sequence to perform numerical product operation to generate a set of interval values. The spacing sequence submodule extracts the timestamp indices corresponding to multiple spacing values ​​based on the spacing value set, pairs and combines the spacing values ​​with the timestamps and writes them into a sorting queue, judges the order of the timestamps in the sorting queue and sorts them in ascending order to generate the initial spacing sequence.

4. The intelligent overhead conveyor system for conveyor belt production according to claim 1, characterized in that, The spacing analysis module includes: The spacing statistics submodule, based on the initial spacing sequence, accumulates multiple spacing values ​​and records the number of elements. It analyzes the mean based on the sum and the number of elements, and simultaneously squares and accumulates the differences between multiple spacings and the mean, calculates the dispersion based on the number of elements, and combines the mean with the upper and lower superposition to generate the spacing benchmark interval. The difference construction submodule calls the initial spacing sequence based on the spacing benchmark interval, extracts adjacent two spacing values ​​according to the sequence index order, performs item-by-item subtraction operation, records each pair of adjacent spacing differences and arranges them according to the original index position to obtain the spacing difference sequence. The mutation location submodule reads the difference elements one by one according to the interval difference sequence and compares them with the set differential mutation threshold. The positions that exceed the differential mutation threshold are marked as mutation point coordinates. At the same time, the preset start state identifier and end state identifier are extracted and combined in coordinate order to obtain the set of aggregation zone positions.

5. The intelligent overhead conveyor system for conveyor belt production according to claim 4, characterized in that, The differential mutation threshold is obtained by acquiring the benchmark spacing test sequence under the standard operating condition of the conveyor and subtracting adjacent terms to obtain the benchmark differential sample set. The mean and dispersion parameters of all differential values ​​in the benchmark differential sample set are statistically analyzed. The dispersion parameters are multiplied with the preset distribution coefficient to generate the fluctuation tolerance limit. The fluctuation tolerance limit is then summed with the mean to determine the threshold.

6. The intelligent overhead conveyor system for conveyor belt production according to claim 1, characterized in that, The density calculation module includes: The spacing filtering submodule collects the associated spacing of the cluster location set and compares it with the set upper and lower limits of the boundary. Based on the multiple associated spacing values ​​and the upper and lower limits of the boundary, it performs interval discrimination item by item and removes out-of-bounds items, extracts the maximum and minimum values, and generates the interval extreme value set. The mapping compression submodule constructs a dual-input data pair based on the interval extreme value set and the associated spacing numerical sequence. It uses the interval extreme values ​​as boundary reference values ​​and performs numerical normalization on each associated spacing. Then, it substitutes the normalization result into the compression expression constructed by the Sigmoid function to map each item and generate a feature parameter sequence. The mean generation submodule performs item-by-item accumulation based on the feature parameter sequence and records the total value. At the same time, it counts the number of parameters in the sequence as the counting benchmark value, and performs a ratio operation between the total value and the counting benchmark value to generate the folding density parameter.

7. The intelligent overhead conveyor system for conveyor belt production according to claim 1, characterized in that, The instruction generation module includes: The hierarchical construction submodule classifies and compares the initial spacing sequence with the spacing benchmark interval to construct a hierarchical distribution set, obtains the multi-element values ​​of the sequence and reads the upper and lower bounds of the mean interval of the spacing benchmark range, performs interval assignment judgment on the element values ​​and the corresponding interval boundaries and extracts extreme values ​​simultaneously to generate a hierarchical distribution frequency set. The status coding submodule performs element frequency statistics and extreme value extraction based on the hierarchical distribution frequency set, calls and sorts the frequency data corresponding to multiple levels, selects the hierarchical index corresponding to the maximum frequency value and extracts extreme value data synchronously, combines them into a dominant status code and matches it with a set status dictionary to obtain the turnout switching level. The level encapsulation submodule compares the folding density parameter with a preset critical danger threshold to determine the voltage mapping range and extracts the motor control voltage based on the range mapping attribute. It then calls the turnout switching level and the motor control voltage to perform data bit splicing and identification assignment operations to generate a transport instruction set.

8. The intelligent overhead conveyor system for conveyor belt production according to claim 7, characterized in that, The critical danger threshold is determined by acquiring the vehicle distribution density data record of the conveyor under extreme load conditions, extracting the minimum safe stopping distance value, calculating the ultimate bearing density base by the ratio of the preset track section length to the minimum safe stopping distance value, and performing a product operation in combination with the preset safety derating multiplier.

9. The intelligent overhead conveyor system for conveyor belt production according to claim 1, characterized in that, The system also includes: The voltage optimization module, based on the transmission instruction set, calculates the voltage deviation coefficient between the motor control voltage and the set operating limit value, adjusts the motor control voltage in reverse compensation according to the voltage deviation coefficient, and reconstructs the data in combination with the turnout switching level to build an optimized transmission coordination instruction. The optimized transmission coordination command includes compensation voltage, coordination control identifier, and reconstructed data frame structure.

10. The intelligent overhead conveyor system for conveyor belt production according to claim 9, characterized in that, The voltage optimization module includes: The deviation coefficient submodule, based on the transmission instruction set, reads the motor control voltage and calculates the difference between the set operating limit value and extracts the voltage offset sequence. It then calculates the normalized ratio between the voltage offset sequence and the set voltage reference amplitude and performs interval mapping to unify the dimensions, thereby generating the voltage deviation coefficient. The compensation voltage submodule, based on the voltage deviation coefficient, calls the original sequence of motor control voltage and performs inverse proportional operation, multiplies the voltage deviation coefficient with the motor control voltage point by point to calculate the compensation voltage component, and simultaneously performs superposition operation with the motor control voltage and performs amplitude limiting to obtain the target motor voltage. The instruction reconstruction submodule, based on the target motor voltage, obtains the turnout switching level and performs synchronization alignment processing, splices the target motor voltage sequence and the turnout switching level, analyzes the multi-dimensional mapping relationship, rearranges the multi-dimensional mapping data structure and reorganizes the encoding format, and generates optimized transport coordination instructions.

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