A plastic product packaging management system based on big data

Abstract

The application discloses a plastic product packaging management system based on big data, and relates to the technical fields of plastic product packaging management and big data processing. The system comprises a data aggregation module, a demand analysis module, a conflict detection module and a production scheduling module. The data aggregation module performs time slicing on data streams of various data sources, splices data records of different data sources in the same time slice into a byte stream, performs a bit folding operation to obtain a folding fingerprint, and maps the folding fingerprint to a ring-shaped storage slot to generate a multi-source data index. The demand analysis module extracts a time slice corresponding to customer order data, reads packaging specification parameters and converts the packaging specification parameters into a coordinate set in a packaging scheme space to generate a candidate packaging scheme set. The conflict detection module performs intersection determination on coordinate ranges of the candidate packaging scheme and material attribute coordinate ranges in material inventory data, and marks feasible packaging schemes.

Description

Technical Field

[0001] This invention relates to the field of plastic product packaging management and big data processing technology, specifically a plastic product packaging management system based on big data. Background Technology

[0002] Plastic product packaging management involves multiple types of data, including customer orders, material inventory, production equipment status, logistics and transportation, and environmental temperature and humidity. These data come from different business systems, with variations in data generation frequency, time granularity, and record format. Current management methods typically store each data source independently. When it's necessary to link customer orders with material inventory or equipment status, cross-database queries are performed using order numbers, material codes, or equipment numbers. Multiple data sources within the same time window lack a unified index. When order volume is large or there are many data sources, frequent cross-database linking operations lead to inconsistent time alignment, easily resulting in material inventory data and customer order data not being from the same time slice. Matching packaging specifications and material inventory attributes often relies on fixed specification numbers or manual judgment. Matching discrete fields fails to reflect the proximity relationships between continuous parameters such as length, width, height, and load-bearing capacity. Inventory materials with similar but not completely identical parameters cannot be automatically identified as usable resources, and some candidate packaging solutions are only found to have unsuitable material attributes after entering production scheduling. Production scheduling typically only inserts tasks based on equipment idle time periods, failing to effectively connect with the feasibility assessment of candidate packaging solution material attributes. Existing technologies have shortcomings in multi-source data time slice indexing, packaging scheme spatial coordinate representation, and coordinate range intersection detection. It is necessary to solve the problems of how to compactly index multi-source data within the same time slice and how to determine the multi-dimensional range intersection of packaging specification parameters and material inventory attributes and screen feasible packaging schemes. Summary of the Invention

[0003] To address the issues of low efficiency in multi-source data association indexing and insufficient accuracy in matching packaging requirements with material inventory attributes in existing plastic product packaging management, this invention provides a big data-based plastic product packaging management system. This system constructs a multi-source data index through time slicing, bit-folding operations, and circular storage slots, and converts packaging specification parameters into a set of spatial coordinates for packaging solutions. Feasible packaging solutions are then selected based on the intersection of coordinate ranges, enabling packaging solution generation, material inventory conflict detection, and production scheduling records to be completed within a unified data processing framework.

[0004] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a big data-based plastic product packaging management system, including a data aggregation module, a demand analysis module, a conflict detection module, and a production scheduling module. The data aggregation module is used to perform time slicing on the data streams from various data sources, concatenating data records from different data sources within the same time slice into a byte stream, performing bit-folding operations on the byte stream to obtain a folded fingerprint, mapping the folded fingerprint onto a circular storage slot, and generating a multi-source data index. The demand analysis module is used to extract the time slices corresponding to customer order data from the multi-source data index, read packaging specification parameters, convert the packaging specification parameters into a coordinate set in the packaging scheme space, and generate a candidate packaging scheme set centered on the coordinate set. The conflict detection module is used to determine the intersection between the coordinate range corresponding to each candidate packaging scheme in the candidate packaging scheme set and the material attribute coordinate range in the material inventory data, marking the candidate packaging schemes that pass the intersection determination as feasible packaging schemes. The production scheduling module is used to insert the feasible packaging schemes into the idle intervals of the production timeline based on the idle periods in the production equipment status data, generating production scheduling records.

[0005] As a technical solution of the present invention, the data aggregation module generates a multi-source data index in the following manner: the data sources include customer order data, material inventory data, production equipment status data, logistics and transportation data, and environmental temperature and humidity data; the data stream of each data source is divided according to a preset time window to obtain multiple time slices; a data source identifier and timestamp are attached to each time slice, and all time slices within the same time window are sorted according to the lexicographical order of the data source identifiers; the data records of the sorted time slices are concatenated into a byte stream, and bit-folding operations are performed on the byte stream to obtain the folded fingerprint; a pointer to the time slice is established according to the mapping position of the folded fingerprint on the circular storage slot, and the pointer is stored in the multi-source data index. Preferably, the lexicographical order is arranged according to the alphabetical order of the data source names, and a sequential number is assigned to each time slice after sorting. The sequential number and the folded fingerprint are used together as the mapping key value of the circular storage slot. This method can uniformly align multi-source heterogeneous data in the time dimension and establish an efficient index through the circular storage slot, improving the efficiency of data location and associated retrieval.

[0006] The requirement analysis module generates a candidate packaging scheme set as follows: It extracts the length, width, height, load-bearing capacity, and material code from the packaging specification parameters of the time slice; maps these values ​​onto the five coordinate axes of the packaging scheme space to obtain initial coordinate points; expands the initial coordinate points by a step size in both the positive and negative directions of each coordinate axis, forming a coordinate set containing multiple expanded coordinate points; and combines all coordinate points in the coordinate set in pairs, generating a candidate packaging scheme for each pair, thus obtaining the candidate packaging scheme set. Preferably, the step size is dynamically determined based on the material shrinkage rate corresponding to the material code. Different material codes correspond to different step size values, and the step size increases with the increase of the material shrinkage rate, thereby making the candidate schemes more closely match the actual physical properties of the material and balancing the packaging size accuracy with the influence of material deformation.

[0007] The conflict detection module performs intersection determination as follows: it reads the coordinate range corresponding to the candidate packaging scheme and the upper and lower boundary values ​​of the coordinate range of each material attribute in the material inventory data; it compares the coordinate range with the upper and lower boundary values ​​to determine whether the coordinate range is completely within the coordinate range of the material attribute; if it is completely within the range, the intersection is determined to be successful, and the corresponding candidate packaging scheme is marked as a feasible packaging scheme; otherwise, the candidate packaging scheme is discarded. This intersection determination can filter out combinations that exceed the material inventory attribute range, ensuring that feasible packaging schemes are executable at the material supply level.

[0008] The production scheduling module generates production schedule records as follows: It extracts the idle time periods for each production device from the production equipment status data, arranges them in chronological order of their start times, and establishes a production timeline; it reads the production equipment identifier and estimated production duration for each feasible packaging scheme from the feasible packaging schemes; it searches for an idle interval corresponding to the production equipment identifier on the production timeline and determines whether the length of this idle interval is not less than the estimated production duration; if there exists an idle interval with the smallest length that is not less than the estimated production duration, the feasible packaging scheme is placed into this idle interval, and the production schedule record is updated. By selecting the minimum matching idle interval, equipment idle fragmentation can be reduced, and the utilization rate of production resources can be improved.

[0009] As a further improvement of the present invention, the system also includes a quality traceability module, used to collect the actual packaging parameters of each finished package during the packaging production process, combine the actual packaging parameters with the identification code of the corresponding feasible packaging scheme to generate a traceability code, and send the traceability code to a distributed storage node. The quality traceability module generates the traceability code by: encoding and compressing each actual value in the actual packaging parameters to obtain a parameter encoding string; concatenating the parameter encoding string with the identification code of the feasible packaging scheme to obtain a concatenated string; performing cyclic redundancy check on the concatenated string to generate a check code; combining the parameter encoding string, the identification code, and the check code in a predetermined order to form the traceability code, and writing the traceability code to the distributed storage node after associating it with the production timestamp. Preferably, the actual packaging parameters include actual length, actual width, actual height, and actual load-bearing capacity. Differential encoding is used when encoding and compressing each actual value, and the difference is calculated and stored based on the corresponding standard parameter value in the feasible packaging scheme. This traceability code generation mechanism provides integrity verification while compressing parameter data, which is beneficial for subsequent quality traceability and anomaly location.

[0010] As another technical solution of the present invention, the system further includes a logistics scheduling module, used to obtain the traceability code from the distributed storage node, and generate a loading sequence based on the destination code and packaging size in the traceability code. The logistics scheduling module generates the loading sequence by: extracting the destination code, packaging size, and packaging weight from the traceability code; reading the remaining volume, remaining load, and arrival time window of the transport vehicle from the logistics transportation data; calculating the volume occupancy value of each packaged product based on the packaging size, and calculating the weight occupancy value of each packaged product based on the packaging weight; combining and matching the volume occupancy values ​​and weight occupancy values ​​of multiple packaged products according to the remaining volume and remaining load of the transport vehicle to obtain multiple loading candidate combinations; and sorting the multiple loading candidate combinations according to the order of the transport vehicle arrival time windows to generate the loading sequence. This method can simultaneously consider volume and weight constraints and match the loading order with the vehicle arrival time, improving logistics loading efficiency and timeliness.

[0011] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0012] After multi-source data is segmented according to a preset time window, data records from different data sources within the same time window are sorted lexicographically by the data source identifier and concatenated into a byte stream. Bit-folding operations are performed on the byte stream to obtain a folded fingerprint, which is then mapped to a circular storage slot and a pointer to the time slice is created. This processing method allows customer orders, material inventory, production equipment status, logistics and transportation, and environmental temperature and humidity data scattered across different data sources to obtain compact index entries within the same time window. The folded fingerprint represents the concatenated byte stream with a fixed length, reducing index storage space and byte stream comparison overhead during lookups. The mapping position on the circular storage slot corresponds the time slice to the folded fingerprint, transforming time-aligned queries of multi-source data into accesses to the mapped positions in the index, avoiding repeated joins across multiple database tables or message queues, and reducing index inconsistencies caused by asynchronous data source times. Time slices with adjacent times are continuously mapped on the circular slot, facilitating the simultaneous reading of related data within adjacent time ranges when extracting the time slice corresponding to customer order data.

[0013] The length, width, height, load-bearing capacity, and material code values ​​in the packaging specifications are mapped to five coordinate axes in the packaging solution space, forming initial coordinate points. A step size is then added around these initial coordinate points in both the positive and negative directions of each coordinate axis, forming a coordinate set. The step size is dynamically determined based on the material shrinkage rate corresponding to the material code, allowing the range of candidate packaging solutions to vary with material characteristics. During the conflict detection phase, the candidate packaging solution set generated from the coordinate set is compared with the material attribute coordinate range in the material inventory data to determine if the candidate solution's coordinate range is completely within the upper and lower boundary values ​​of the material attribute coordinate range. This method transforms discrete specification matching into continuous range intersection determination in a multi-dimensional coordinate space. The proximity relationships between parameters such as length, width, height, and load-bearing capacity are preserved on the coordinate axes, avoiding missed detections caused by fixed specification numbers or manual experience matching for usable material inventory with similar parameters. Simultaneously, candidate solutions completely outside the material attribute coordinate range are directly discarded, reducing the number of infeasible packaging solutions entering production scheduling. The production scheduling module only matches available intervals for packaging schemes marked as feasible, ensuring that scheduling records are aligned with the feasible range of material inventory and reducing redundant adjustments caused by material attribute conflicts during the scheduling phase. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0015] Figure 1This is a schematic diagram of the structure of a big data-based plastic product packaging management system;

[0016] Figure 2 This is a flowchart of the data aggregation module generating multi-source data indexes;

[0017] Figure 3 This is a flowchart illustrating the process by which the requirements analysis module generates a set of candidate packaging solutions. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 This invention provides a big data-based plastic product packaging management system, including a data aggregation module, a demand analysis module, a conflict detection module, and a production scheduling module. The data aggregation module is connected to the demand analysis module, which in turn is connected to the conflict detection module, and the conflict detection module is connected to the production scheduling module. The data aggregation module performs time slicing on the data streams from various data sources, concatenates data records from different data sources within the same time slice into a byte stream, performs bit-folding operations on the byte stream to obtain folded fingerprints, maps the folded fingerprints to a circular storage slot, and generates a multi-source data index. The demand analysis module extracts the time slices corresponding to customer order data from the multi-source data index, reads packaging specification parameters, converts the packaging specification parameters into a coordinate set in the packaging scheme space, and generates a candidate packaging scheme set centered on the coordinate set. The conflict detection module performs intersection checks on the coordinate range corresponding to each candidate packaging scheme in the candidate packaging scheme set and the material attribute coordinate range in the material inventory data, marking the candidate packaging schemes that pass the intersection check as feasible packaging schemes. The production scheduling module inserts feasible packaging schemes into the idle intervals of the production timeline based on the idle periods in the production equipment status data, generating production scheduling records.

[0020] See Figure 2 This invention provides a big data-based plastic product packaging management system, including a data aggregation module, a demand analysis module, a conflict detection module, and a production scheduling module. The data aggregation module performs time-slicing on the data streams from various data sources, concatenates data records from different data sources within the same time slice into a byte stream, performs bit-folding operations on the byte stream to obtain a folded fingerprint, maps the folded fingerprint to a circular storage slot, and generates a multi-source data index. The specific implementation of the data aggregation module is as follows.

[0021] The data aggregation module acquires data from sources including customer order data, material inventory data, production equipment status data, logistics and transportation data, and environmental temperature and humidity data. Each data source continuously generates data records in the form of a data stream.

[0022] In practice, the data aggregation module segments the data stream from each data source according to a preset time window. The time window is set to a fixed length of 60 seconds. Each time a segment is created, all data records within the current time window are grouped into the same time slice. For each time slice, the data aggregation module adds a data source identifier and a timestamp. The data source identifier distinguishes between customer order data, material inventory data, production equipment status data, logistics and transportation data, and environmental temperature and humidity data. The timestamp records the start time of the corresponding time slice.

[0023] The data aggregation module sorts all time slices within the same time window according to the lexicographical order of the data source identifiers. The lexicographical order follows the alphabetical order of the data source names. After sorting, the data aggregation module assigns a sequence number to each time slice. The sequence numbers start from 1 and increment sequentially; the index of each time slice in the sorting result serves as its sequence number.

[0024] The sorted time slice data records are concatenated into a byte stream. During concatenation, the data records of each time slice are read sequentially according to the sorting result, and the binary representations of each data record are concatenated to form a complete byte stream. The length of the byte stream varies with the number of time slices and the content of the data records.

[0025] Perform bit-folding operations on the byte stream. Bit-folding divides the byte stream into multiple data segments of a fixed bit width. Each segment is then XORed with a bitwise AND operation to obtain a fixed-length folded fingerprint. The fixed bit width is set to 64 bits. When dividing the data segments, if the byte stream length is not an integer multiple of the fixed bit width, zero bytes are padded to the end of the last data segment until the fixed bit width is reached. The bitwise XOR operation XORs bits at the same position to obtain the folded fingerprint. The length of the folded fingerprint is equal to the fixed bit width.

[0026] A pointer to the time slice is established based on the mapping position of the folded fingerprint on the circular storage slot. The circular storage slot consists of multiple storage slots arranged in a circular order. The mapping position is calculated using the following formula:

[0027]

[0028] in, This indicates the mapped position on the circular storage slot, with a value ranging from 0 to... ; This represents the integer value corresponding to the folded fingerprint, ranging from 0 to... ; The integer value representing the sequential number of the time slice, with a range of positive integers starting from 1; The total number of circular storage slots is a pre-defined positive integer. The integer value corresponding to the folded fingerprint is obtained by interpreting the binary bits of the folded fingerprint into an unsigned integer in descending order of the high-order bits. The integer value corresponding to the sequence number is the sequence number itself. The total number of circular storage slots is determined based on the index storage capacity and is set to 1024.

[0029] After calculating the mapped location, the data aggregation module writes a pointer to the corresponding storage slot on the circular storage slot. The pointer points to the starting address of the corresponding time slice in the data storage area. The pointer is then stored in the multi-source data index. The multi-source data index contains multiple index entries, each including the mapped location, pointer, data source identifier, timestamp, and sequence number. The multi-source data index allows for the location of data records from different data sources within the same time slice.

[0030] In some embodiments, before writing the pointer, the data aggregation module checks whether the storage slot corresponding to the mapped position on the circular storage slot already has a pointer. If a pointer already exists, a pointer linked list is formed in the storage slot corresponding to the mapped position, and the new pointer is linked after the existing pointer. When reading a time slice, the pointer linked list is traversed according to the mapped position to obtain all pointers pointing to the time slice.

[0031] In some embodiments, the data aggregation module configures a fixed bit width. When the fixed bit width is set to 64 bits, the folded fingerprint occupies 8 bytes. When the fixed bit width is set to 128 bits, the folded fingerprint occupies 16 bytes. The larger the fixed bit width, the lower the collision probability of the folded fingerprint, but the larger the storage space occupied by the folded fingerprint.

[0032] In some embodiments, the data aggregation module adjusts the time window size based on the arrival frequency of customer order data, material inventory data, production equipment status data, logistics and transportation data, and environmental temperature and humidity data. When the arrival frequency of data from each data source is high, the time window size is reduced; when the arrival frequency of data from each data source is low, the time window size is increased. After adjusting the time window size, time slicing, sorting, splicing, bit-folding operations, and mapping operations are re-executed.

[0033] In some embodiments, the data aggregation module sorts all time slices within the same time window according to the lexicographical order of the data source identifiers. If the data source names are the same, they are arranged according to the order of the timestamps. The sorting result is uniquely determined. The sequence numbers are assigned sequentially, starting from 1, without repetition.

[0034] In some embodiments, when concatenating byte streams, the data aggregation module converts each data record into a UTF-8 encoded byte sequence, and then concatenates the byte sequences sequentially according to their sorting results. UTF-8 encoding can represent Chinese and English characters in customer order data, material inventory data, production equipment status data, logistics and transportation data, and environmental temperature and humidity data.

[0035] In some embodiments, before performing bit-folding operations, the data aggregation module performs hash padding on the byte stream, padding the byte stream length to an integer multiple of the fixed bit width. Hash padding uses the SHA-256 hash function to calculate a hash value on the original byte stream, and appends the first few bytes of the hash value to the end of the original byte stream, making the length of the padded byte stream an integer multiple of the fixed bit width. Bit-folding operations are then performed on the padded byte stream.

[0036] In some embodiments, when the data aggregation module maps folded fingerprints to circular storage slots, it simultaneously combines the folded fingerprint, sequence number, and data source identifier into a mapping key value, and calculates the mapping position using this key value. The mapping key value is calculated by converting the folded fingerprint and sequence number to decimal strings, and the data source identifier to ASCII strings. These three strings are then concatenated in the order of folded fingerprint, sequence number, and data source identifier. A CRC32 checksum is then applied to the concatenated string to obtain the mapping key value. The mapping position is obtained by taking the mapping key value modulo the total number of circular storage slots. This method reduces the likelihood of identical mapping positions when folded fingerprints are the same but sequence numbers are different.

[0037] In some embodiments, the data aggregation module periodically reorganizes the multi-source data index. When the length of the pointer list of any storage slot in the circular storage slot exceeds a preset threshold, the data aggregation module remaps the pointers in the storage slot corresponding to the mapped position to the expanded circular storage slot. The total number of expanded circular storage slots is doubled, and the mapping positions are recalculated.

[0038] In some embodiments, after generating the multi-source data index, the data aggregation module stores the multi-source data index in memory and writes a copy of the multi-source data index to a distributed storage node. The distributed storage node is used to persist the multi-source data index and prevent its loss after the data aggregation module restarts.

[0039] In some embodiments, timestamps use Unix timestamps, accurate to milliseconds. Time slices within the same time window have the same initial timestamp value. Data source identifiers use preset enumeration values: customer order data corresponds to enumeration value 1, material inventory data to enumeration value 2, production equipment status data to enumeration value 3, logistics and transportation data to enumeration value 4, and environmental temperature and humidity data to enumeration value 5. When the lexicographical order is based on the alphabetical order of the data source names, customer order data, material inventory data, production equipment status data, logistics and transportation data, and environmental temperature and humidity data correspond to the alphabetical order C, H, S, W, and H, respectively. Since the first letter of both material inventory data and environmental temperature and humidity data names is H, they are arranged alphabetically by their full names, with material inventory data appearing before environmental temperature and humidity data.

[0040] See Figure 3 The specific implementation of the requirements analysis module is as follows.

[0041] The requirement parsing module extracts the time slices corresponding to customer order data from the multi-source data index. The multi-source data index contains multiple index entries, each including a mapping location, pointer, data source identifier, timestamp, and sequence number. The requirement parsing module iterates through the multi-source data index, filtering out all index entries whose data source identifier is customer order data. For each filtered index entry, the requirement parsing module reads the pointer, which points to the starting address of the time slice corresponding to the customer order data in the data storage area. Based on the pointer, the requirement parsing module reads the data records within the time slice and parses the data records into multiple fields.

[0042] In practical implementation, the requirements parsing module reads the packaging specification parameters. These parameters include length, width, height, load-bearing capacity, and material code. The length value represents the length of the plastic product packaging in millimeters; the width value represents the width of the plastic product packaging in millimeters; the height value represents the height of the plastic product packaging in millimeters; the load-bearing capacity value represents the maximum weight the plastic product packaging can withstand in kilograms; and the material code represents the type of material used in the plastic product packaging, which is a preset enumerated value.

[0043] The requirements parsing module maps length, width, height, load-bearing capacity, and material codes to five coordinate axes in the packaging solution space. The packaging solution space is a five-dimensional space: the first axis corresponds to length, the second to width, the third to height, the fourth to load-bearing capacity, and the fifth to the material code. The length value is mapped to the first axis, the width to the second, the height to the third, the load-bearing capacity to the fourth, and the material code to the fifth. The value corresponding to the material code is determined by a pre-defined material code mapping table, which maps each material code to an integer.

[0044] After mapping the five coordinate axes, the initial coordinate point is obtained. The coordinates of the initial coordinate point in the packaging scheme space are (the coordinate value corresponding to the length value, the coordinate value corresponding to the width value, the coordinate value corresponding to the height value, the coordinate value corresponding to the load-bearing value, and the coordinate value corresponding to the material code).

[0045] Expanding the coordinates around the initial coordinate point by one step in each positive and negative direction on each coordinate axis results in a coordinate set containing multiple expanded coordinate points. For the first coordinate axis, the expanded coordinate values ​​include the coordinate value corresponding to the length value minus the step size and the coordinate value corresponding to the length value plus the step size; for the second coordinate axis, the expanded coordinate values ​​include the coordinate value corresponding to the width value minus the step size and the coordinate value corresponding to the width value plus the step size; for the third coordinate axis, the expanded coordinate values ​​include the coordinate value corresponding to the height value minus the step size and the coordinate value corresponding to the height value plus the step size; for the fourth coordinate axis, the expanded coordinate values ​​include the coordinate value corresponding to the load-bearing value minus the step size and the coordinate value corresponding to the load-bearing value plus the step size; for the fifth coordinate axis, the expanded coordinate values ​​include the coordinate value corresponding to the material code minus the step size and the coordinate value corresponding to the material code plus the step size. After expanding the coordinate values ​​on each coordinate axis by one step size in each positive and negative direction from the initial coordinate point, three coordinate values ​​are formed on the corresponding coordinate axis: initial coordinate value minus step size, initial coordinate value, and initial coordinate value plus step size. Each of the five coordinate axes has three optional coordinate values, and the coordinate set contains the Cartesian product of all the optional coordinate values ​​on the five coordinate axes. The number of extended coordinate points in the coordinate set is 3 to the power of 5, or 243 extended coordinate points.

[0046] The step size is dynamically determined based on the material shrinkage rate corresponding to the material code. The material shrinkage rate represents the percentage of dimensional shrinkage of the plastic material during the cooling and molding process. The requirements parsing module queries the material shrinkage rate database based on the material code. This database stores different material codes and their corresponding material shrinkage rates. The step size is determined by the following formula:

[0047]

[0048] in, This indicates the expansion step size on each coordinate axis, in coordinate axis units; This indicates the step size scaling factor, which is set to 3. This represents the material shrinkage rate corresponding to the material code, with a value ranging from 0 to 0.3. It is obtained by querying the material shrinkage rate database using the material code. The step size ratio coefficient is set to 3 because: the material shrinkage rate ranges from 0 to 0.3, and multiplying by 3 expands the step size range to 0 to 0.9. This can generate enough candidate coordinate points near the initial coordinate point to cover the dimensional deviation caused by material shrinkage, without producing an excessively large coordinate expansion range.

[0049] In practice, different material codes correspond to different step size values. When the material code is polyethylene, the shrinkage rate of polyethylene recorded in the material shrinkage rate database is 0.020, and the step size is [value missing]. When the material code is polypropylene, the shrinkage rate of polypropylene recorded in the material shrinkage database is 0.018, and the step size is [value missing]. When the material code is polyvinyl chloride (PVC), the shrinkage rate of PVC recorded in the material shrinkage rate database is 0.015, and the step size is [value missing]. The step size increases as the material shrinkage rate increases.

[0050] The requirements analysis module pairs all coordinate points in the coordinate set. The pairwise pairing method involves randomly selecting two different extended coordinate points from the coordinate set to form a combination. The two extended coordinate points in each combination serve as the two key coordinate points for each candidate packaging solution. The total number of combinations is the square of the number of extended coordinate points in the coordinate set minus the total number of extended coordinate points in the coordinate set. The subtraction of the number of extended coordinate points in the coordinate set is to exclude cases where two extended coordinate points are identical. Each combination generates a candidate packaging scheme, which includes the packaging specification parameter combination corresponding to the two extended coordinate points. This yields the candidate packaging scheme set.

[0051] In some embodiments, when the requirement parsing module expands by one step in each positive and negative direction around the initial coordinate point, it rounds the coordinate values ​​on each coordinate axis to two decimal places. Rounding is used to reduce the precision loss of the expanded coordinate points.

[0052] In some embodiments, the requirement parsing module uses a material code mapping table when mapping material codes to coordinate values ​​on the fifth coordinate axis. The material code mapping table stores the correspondence between material codes and integer values. Polyethylene material code is mapped to 1, polypropylene material code to 2, polyvinyl chloride material code to 3, and polystyrene material code to 4. The integer values ​​corresponding to the material codes are used as the coordinate values ​​on the fifth coordinate axis.

[0053] In some embodiments, when reading packaging specification parameters, the requirement parsing module performs data type conversion on the length, width, height, load-bearing capacity, and material code. The length, width, height, and load-bearing capacity are converted to double-precision floating-point numbers, and the material code is converted to an integer. The parameters after data type conversion are used for subsequent coordinate mapping.

[0054] In some embodiments, when the requirements parsing module reads packaging specification parameters from a time slice, if a parameter is missing from the time slice, it is filled with a default value. The default values ​​for length, width, height, load capacity, and material code are 0. After the default values ​​are filled, subsequent processing continues.

[0055] In some embodiments, the requirement parsing module performs deduplication on the candidate packaging scheme set. Two candidate packaging schemes containing the same two extended coordinate points in reverse order are considered the same candidate packaging scheme. Deduplication is performed by sorting the two extended coordinate points in each candidate packaging scheme in ascending order of coordinate values, and then comparing whether the candidate packaging schemes are completely identical. The deduplicated candidate packaging scheme set is the final candidate packaging scheme set.

[0056] The specific implementation of the conflict detection module is as follows.

[0057] In practice, the conflict detection module reads the coordinate range corresponding to the candidate packaging scheme. This coordinate range is formed by taking the minimum and maximum values ​​of the two extended coordinate points contained within the candidate packaging scheme on the five coordinate axes. The five coordinate axes are the first, second, third, fourth, and fifth axes. The first axis corresponds to the length value, the second to the width value, the third to the height value, the fourth to the load-bearing capacity value, and the fifth to the material code. For the first axis, the lower bound of the coordinate range corresponding to the candidate packaging scheme is the smaller coordinate value of the two extended coordinate points on the first axis, and the upper bound is the larger coordinate value of the two extended coordinate points on the first axis. For the second, third, fourth, and fifth axes, the conflict detection module determines the lower and upper bounds of the coordinate range corresponding to the candidate packaging scheme in the same way.

[0058] The conflict detection module reads the upper and lower boundary values ​​of the coordinate range for each material attribute in the material inventory data. Each material record in the material inventory data contains a coordinate range for its material attributes, which has upper and lower boundary values ​​on five coordinate axes. The conflict detection module reads the upper and lower boundary values ​​of the material attribute coordinate range on the first, second, third, fourth, and fifth coordinate axes.

[0059] The conflict detection module compares the coordinate range corresponding to the candidate packaging scheme with the upper and lower boundary values ​​of the material property coordinate range. The comparison method is as follows: for each of the five coordinate axes, it checks whether the lower boundary value of the coordinate range corresponding to the candidate packaging scheme is not less than the lower boundary value of the material property coordinate range, and whether the upper boundary value of the coordinate range corresponding to the candidate packaging scheme is not greater than the upper boundary value of the material property coordinate range. When the comparison results of all five coordinate axes satisfy the condition that the lower boundary value of the coordinate range corresponding to the candidate packaging scheme is not less than the lower boundary value of the material property coordinate range and the upper boundary value of the coordinate range corresponding to the candidate packaging scheme is not greater than the upper boundary value of the material property coordinate range, the coordinate range corresponding to the candidate packaging scheme is determined to be completely within the material property coordinate range. If it is completely within the range, the intersection is considered successful, and the corresponding candidate packaging scheme is marked as a feasible packaging scheme. If the comparison result of any coordinate axis does not satisfy the condition that the lower boundary value of the coordinate range corresponding to the candidate packaging scheme is not less than the lower boundary value of the material property coordinate range, or the upper boundary value of the coordinate range corresponding to the candidate packaging scheme is not greater than the upper boundary value of the material property coordinate range, then it is determined that the coordinate range corresponding to the candidate packaging scheme is not completely within the material property coordinate range, and the conflict detection module discards the corresponding candidate packaging scheme.

[0060] In some embodiments, the conflict detection module sequentially performs intersection checks on multiple material attribute coordinate ranges. An intersection is considered successful when the coordinate range corresponding to a candidate packaging scheme completely satisfies the containment relationship with any material attribute coordinate range. The material inventory data contains multiple material records, and the conflict detection module traverses all material attribute coordinate ranges.

[0061] Optionally, the conflict detection module uses a floating-point comparison method with a comparison precision of 0.01. When the difference between the lower bound of the coordinate range corresponding to the candidate packaging scheme and the lower bound of the material property coordinate range is greater than negative 0.01, the lower bound of the coordinate range corresponding to the candidate packaging scheme is determined to be no less than the lower bound of the material property coordinate range. When the difference between the upper bound of the coordinate range corresponding to the candidate packaging scheme and the upper bound of the material property coordinate range is less than 0.01, the upper bound of the coordinate range corresponding to the candidate packaging scheme is determined to be no greater than the upper bound of the material property coordinate range.

[0062] The specific implementation method of the production scheduling module is as follows.

[0063] In practice, the production scheduling module extracts the idle time periods for each production device from the production equipment status data. The production equipment status data includes the production equipment identifier, the start time of the idle time period, and the end time of the idle time period. The production scheduling module arranges all idle time periods in chronological order of their start times, establishing a production timeline. The production timeline is a time axis, and idle time periods are marked on it.

[0064] The production scheduling module reads the production equipment identifier and estimated production time for each feasible packaging plan from the feasible packaging plan list. Feasible packaging plans are candidate packaging plans marked by the conflict detection module. The production equipment identifier specifies the production equipment to execute the feasible packaging plan. The estimated production time is the time required for the feasible packaging plan to complete production, in minutes.

[0065] The production scheduling module searches for idle intervals corresponding to production equipment identifiers on the production timeline. Idle intervals are determined by the idle time periods belonging to the same production equipment identifier on the production timeline. The production scheduling module then determines whether the length of the idle interval is not less than the expected production duration. This determination is made using the following formula:

[0066]

[0067] in, This represents the difference between the available time of the idle period and the expected production time, in minutes, and can be positive, zero, or negative. This represents the length of the idle interval corresponding to the production equipment identifier on the production timeline, in minutes, and is obtained by subtracting the start time from the end time of the idle interval. This indicates the estimated production time for feasible packaging solutions, in minutes. When the value is not less than zero, it means that the length of the idle interval is not less than the expected production time.

[0068] If there exists a free interval with a minimum length that is not less than the estimated production time, then the feasible packaging solution is placed within that free interval. The free interval with a minimum length that is not less than the estimated production time is... Not less than zero and The idle interval with the smallest value is selected. After a feasible packaging solution is placed in the selected idle interval, the production scheduling module updates the production scheduling record. The production scheduling record includes the production equipment identifier, feasible packaging solution identifier, production start time, production end time, and estimated production duration. The production start time is the start time of the idle interval, and the production end time is the production start time plus the estimated production duration.

[0069] In some embodiments, when there are multiple idle intervals corresponding to production equipment identifiers on the production timeline, the production scheduling module calculates the idle interval for each idle interval. ,choose Not less than zero and The smallest free interval. If multiple free intervals exist. If there are the smallest and equal free intervals, then the free interval with the earliest start time is selected.

[0070] Optionally, after updating the production scheduling record, the production scheduling module synchronously updates the corresponding idle time period in the production equipment status data, removes the occupied idle interval from the idle time period, and re-establishes the production timeline for the insertion of subsequent feasible packaging schemes.

[0071] It is understandable that when the production scheduling module processes each feasible packaging plan, it searches for available intervals and updates the production scheduling records in the manner described above.

[0072] The specific implementation method of the quality traceability module is as follows.

[0073] In practice, the quality traceability module collects the actual packaging parameters of each finished product during the packaging production process. These actual packaging parameters include actual length, actual width, actual height, and actual load-bearing capacity. The actual length, measured in millimeters, represents the length of the finished product after production; the actual width, measured in millimeters; the actual height, measured in millimeters; and the actual load-bearing capacity, measured in kilograms. These parameters are collected by length, width, height, and pressure sensors located on the packaging production line, and are transmitted to the quality traceability module via a data interface.

[0074] Differential encoding is used when encoding and compressing each actual value in the actual packaging parameters. The difference is calculated and stored based on the corresponding standard parameter value in the feasible packaging scheme. The corresponding standard parameter values ​​in the feasible packaging scheme include standard length, standard width, standard height, and standard load-bearing capacity. The standard length value is the length corresponding to the first coordinate axis in the feasible packaging scheme; the standard width value is the width corresponding to the second coordinate axis; the standard height value is the height corresponding to the third coordinate axis; and the standard load-bearing capacity value is the load-bearing capacity corresponding to the fourth coordinate axis. The differential encoding method calculates the difference between each actual packaging parameter and its corresponding standard parameter value using the following formula:

[0075]

[0076] in, This represents the difference between any actual packaging parameter and the corresponding standard parameter value in the feasible packaging scheme, with the unit being the unit corresponding to the actual packaging parameter. This represents the numerical value of any actual packaging parameter, with the unit corresponding to that actual packaging parameter. Indicating feasible packaging solutions with The corresponding standard parameter values, in units of 1 and 2. The same units are used. The difference between the actual length and the standard length is calculated, the difference between the actual width and the standard width is calculated, the difference between the actual height and the standard height is calculated, and the difference between the actual load-bearing capacity and the standard load-bearing capacity is calculated.

[0077] In implementation, the quality traceability module encodes the length difference, width difference, height difference, and load-bearing difference into fixed-length binary strings. Since the difference between the actual packaging parameters and the standard parameter values ​​ranges from -32768 to 32767, each difference is represented using 16-bit signed two's complement. The length difference is encoded as a 16-bit binary string, the width difference as a 16-bit binary string, the height difference as a 16-bit binary string, and the load-bearing difference as a 16-bit binary string. The quality traceability module concatenates these codes sequentially, with the length difference code first, followed by the width difference code, then the height difference code, and finally the load-bearing difference code, to obtain the parameter encoding string. The total length of the parameter encoding string is 64 bits.

[0078] Optionally, the length difference encoding, width difference encoding, height difference encoding, and load-bearing difference encoding in the parameter encoding string are all stored in little-endian byte order. The least significant byte in little-endian byte order is stored at the lowest address, and the most significant byte is stored at the highest address.

[0079] In some embodiments, before encoding and compressing each actual packaging parameter, the quality traceability module first unifies the dimensions of the actual length, width, height, and load-bearing capacity. The actual length, width, and height values ​​are uniformly converted to millimeters, and the actual load-bearing capacity value is uniformly converted to kilograms. The dimension-unified actual packaging parameters are then compared with the corresponding standard parameter values ​​in the feasible packaging scheme.

[0080] The quality traceability module concatenates the parameter encoding string with the identification code of the corresponding feasible packaging scheme to obtain a concatenated string. The identification code of the corresponding feasible packaging scheme is a unique identifier generated by the conflict detection module when marking feasible packaging schemes, and the identifier code is represented by a 32-bit binary string. During concatenation, the parameter encoding string is located in the first 64 bits of the concatenated string, and the identifier code is located in the last 32 bits of the concatenated string, with a total length of 96 bits.

[0081] The concatenated string undergoes cyclic redundancy check (CRC) to generate a checksum. The quality traceability module employs the CRC-16 / CCITT cyclic redundancy check algorithm, generating the polynomial as follows: The concatenated string is input to the Cyclic Redundancy Check (CRBC) calculation unit. The CRBC calculation unit performs shift and XOR operations on each bit of the concatenated string, resulting in a 16-bit checksum. The initial value of the CRBC calculation unit is set to 0xFFFF, and input and output data inversion is not used during the calculation.

[0082] In practice, the Cyclic Redundancy Check (CRC) calculation process is as follows: The concatenated string is sequentially fed into the CRC calculation unit in byte order. The CRC calculation unit maintains a 16-bit register with an initial value of 0xFFFF. For each byte fed in, the CRC calculation unit XORs the byte with the high 8 bits of the 16-bit register, then shifts the 16-bit register left by 8 bits, and performs a conditional XOR on the shifted-out bits according to the generator polynomial. This process is repeated 8 times to complete the check calculation for one byte. After all bytes have been processed, the value in the 16-bit register is the checksum.

[0083] In some embodiments, the quality traceability module employs the CRC-32 cyclic redundancy check algorithm, generating the polynomial as follows: The initial value is set to 0xFFFFFFFF. After the calculation is complete, the result is inverted bit by bit to obtain a 32-bit checksum. At this point, the total length of the concatenated string is 96 bits, the checksum length is 32 bits, and the total length of the traceability code is 128 bits.

[0084] The quality traceability module combines the parameter encoding string, identification code, and check code into a traceability code in a predetermined order. The predetermined order is: the parameter encoding string is in the first 64 bits of the traceability code, the identification code is in the middle 32 bits, and the check code is in the last 16 bits. The parameter encoding string, identification code, and check code are concatenated bit by bit to obtain the traceability code. When using the CRC-16 / CCITT cyclic redundancy check algorithm, the total length of the traceability code is 112 bits.

[0085] The traceability code is associated with the production timestamp and then written to the distributed storage node. The production timestamp is the system timestamp at the moment the traceability code is generated by the quality traceability module, using the Unix timestamp format and accurate to milliseconds. The quality traceability module combines the traceability code and the production timestamp into a key-value pair, with the traceability code as the key and the production timestamp as the value. The quality traceability module then writes the key-value pair to the distributed storage node, which is a distributed key-value storage node that uses a consistent hashing algorithm to distribute the key-value pairs.

[0086] In some embodiments, the quality traceability module associates the traceability code with the production timestamp and simultaneously writes the traceability code into a traceability code index table in a distributed storage node. The traceability code index table arranges the traceability codes in order of production timestamp, facilitating subsequent lookup of traceability codes by time range.

[0087] It is understandable that the quality traceability module performs the complete process of collecting actual packaging parameters, differential coding, splicing, cyclic redundancy check, and writing to distributed storage nodes for each packaged product.

[0088] The specific implementation method of the logistics scheduling module is as follows.

[0089] In practice, the logistics scheduling module retrieves traceability codes from distributed storage nodes. These nodes store key-value pairs associated with the traceability codes and production timestamps. The logistics scheduling module sends traceability code query requests to the distributed storage nodes, which then return the corresponding traceability codes, which the logistics scheduling module receives.

[0090] The logistics scheduling module extracts the destination code, packaging dimensions, and packaging weight from the traceability code. The traceability code contains a parameter code string, an identification code, a check code, and a destination code in a predetermined order. The logistics scheduling module segments these components from the traceability code in a predetermined order. The destination code is a byte sequence following the check code in the traceability code, indicating the destination to which the packaged product needs to be transported. The packaging dimensions are obtained by querying the feasible packaging plan record using the identification code. The feasible packaging plan record stores packaging dimension parameters and packaging weight parameters. Packaging dimensions include packaging length, packaging width, and packaging height values. The packaging length value represents the external length of the packaged product, the packaging width value represents the external width of the packaged product, and the packaging height value represents the external height of the packaged product. The packaging weight represents the overall weight of the packaged product. The logistics scheduling module reads the packaging length value, packaging width value, packaging height value, and packaging weight from the feasible packaging plan record.

[0091] The logistics dispatch module reads the remaining volume, remaining load capacity, and arrival time window of transport vehicles from the logistics transportation data. The logistics transportation data includes the vehicle identifier, remaining volume, remaining load capacity, and arrival time window. The logistics dispatch module reads the remaining volume, remaining load capacity, and arrival time window corresponding to each transport vehicle identifier from the logistics transportation data. Remaining volume represents the unused space in the transport vehicle's compartment, in cubic meters. Remaining load capacity represents the unused carrying capacity of the transport vehicle, in kilograms. The arrival time window includes the arrival start time and arrival end time; the arrival start time indicates the earliest time the transport vehicle arrives at the loading point, and the arrival end time indicates the latest time the transport vehicle arrives at the loading point.

[0092] In practice, the logistics scheduling module calculates the volumetric occupancy of each packaged product based on its dimensions. The volumetric occupancy is calculated using the following formula:

[0093]

[0094] in, This represents the volume occupied by the packaged product, in cubic meters, and is obtained by multiplying the package length, width, and height. This indicates the packaging length value, extracted from the traceability code, in meters, and the value range is greater than 0. This represents the packaging width value, extracted from the traceability code, in meters, and the value range is greater than 0. This indicates the packaging height value, extracted from the traceability code, in meters, and the value range is greater than 0.

[0095] In practice, the packaging length, width, and height values ​​are extracted from the traceability code and stored in millimeters. The logistics scheduling module divides the original packaging length value by 1000 to obtain the packaging length value in meters, the original packaging width value by 1000 to obtain the packaging width value in meters, and the original packaging height value by 1000 to obtain the packaging height value in meters. Substituting the package length, width, and height values ​​(divided by 1000) into the above formula, the volume occupancy value is calculated.

[0096] The logistics scheduling module calculates the weight occupancy value for each packaged product based on its packaging weight. The weight occupancy value is directly taken from the packaging weight. The packaging weight is read from the feasible packaging plan record, and the raw value is stored in kilograms. The logistics scheduling module uses the read packaging weight as the weight occupancy value, with the unit being kilograms and a value greater than 0.

[0097] In practice, the logistics scheduling module combines and matches the volume and weight occupancy values ​​of multiple packaged products according to the remaining volume and weight capacity of the transport vehicles. For each transport vehicle, the logistics scheduling module performs the following operations: It reads each packaged product from the set of products to be loaded, each with a corresponding volume and weight occupancy value. The module maintains the cumulative volume and weight occupancy values ​​for the current loading candidate combinations, initially empty with both the cumulative volume and weight occupancy values ​​set to 0. The module reads a packaged product, adds its volume occupancy value to the current loading candidate combination's cumulative volume occupancy value, and adds its weight occupancy value to the packaged product's cumulative weight occupancy value, resulting in a new cumulative weight occupancy value. The module then determines whether the new cumulative volume and weight occupancy values ​​are greater than the transport vehicle's remaining volume and weight capacity. If the new cumulative volume occupancy value is not greater than the remaining volume of the transport vehicle and the new cumulative weight occupancy value is not greater than the remaining load capacity of the transport vehicle, then the packaged product is added to the current loading candidate combination, and the cumulative volume occupancy value and cumulative weight occupancy value of the current loading candidate combination are updated to the new cumulative volume occupancy value. If the new cumulative volume occupancy value is greater than the remaining volume of the transport vehicle or the new cumulative weight occupancy value is greater than the remaining load capacity of the transport vehicle, then the packaged product is not added to the current loading candidate combination, and the next packaged product is read. When the logistics scheduling module has traversed the set of packaged products to be loaded or the remaining volume and remaining load capacity of the transport vehicle are insufficient to accommodate any more packaged products, all packaged products already added to the current loading candidate combination constitute a loading candidate combination. The logistics scheduling module performs the above operations for each transport vehicle, obtaining multiple loading candidate combinations. Each loading candidate combination corresponds to one transport vehicle and contains the set of packaged products that need to be loaded on that transport vehicle.

[0098] In some embodiments, during the combination and matching process, the logistics scheduling module sorts the set of packaged finished products to be loaded according to their volume occupancy value from largest to smallest. After sorting, the packaged finished products are read one by one in descending order of volume occupancy value for combination and matching. This ensures that packaged finished products with larger volume occupancy values ​​are preferentially matched with the remaining volume of the transport vehicle.

[0099] In some embodiments, during the combination and matching process, the logistics scheduling module sorts the set of packaged finished products to be loaded according to their weight occupancy value from largest to smallest. After sorting, the packaged finished products are read one by one in descending order of weight occupancy value for combination and matching. This ensures that packaged finished products with larger weight occupancy values ​​are preferentially matched with the remaining load capacity of the transport vehicle.

[0100] The logistics scheduling module sorts multiple loading candidate combinations according to the arrival time windows of the transport vehicles. The sorting compares the arrival start times of the transport vehicles corresponding to each loading candidate combination. The loading candidate combination corresponding to the transport vehicle with the earliest arrival start time is placed first in the loading sequence, the loading candidate combination corresponding to the transport vehicle with the second earliest arrival start time is placed second in the loading sequence, and so on. When two transport vehicles have the same arrival start time, the loading candidate combinations are arranged in lexicographical order according to the transport vehicle identifiers. The sorted loading candidate combination sequence generates a loading sequence. Each element in the loading sequence includes the transport vehicle identifier, the loading candidate combination, the start time of the arrival time window, and the end time of the arrival time window. The logistics scheduling module outputs the loading sequence for the transportation execution system to load the packaged finished products according to the loading sequence.

[0101] Optionally, after generating the loading sequence, the logistics scheduling module writes the loading sequence to the production scheduling record or a distributed storage node. The write operation persists the loading sequence for subsequent querying and scheduling.

[0102] It is understandable that the logistics scheduling module performs a complete process of obtaining and extracting traceability codes, calculating volume occupancy values, calculating weight occupancy values, combining and matching, and sorting for each packaged finished product to be shipped.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A big data-based plastic product packaging management system, characterized in that, include: The data aggregation module is used to perform time slicing on the data streams from various data sources, splice data records from different data sources within the same time slice into a byte stream, perform bit folding operation on the byte stream to obtain a folded fingerprint, and map the folded fingerprint onto a circular storage slot to generate a multi-source data index. The demand parsing module is used to extract time slices corresponding to customer order data from the multi-source data index, read packaging specification parameters, convert the packaging specification parameters into a coordinate set in the packaging scheme space, and generate a candidate packaging scheme set with the coordinate set as the center. The conflict detection module is used to determine the intersection between the coordinate range corresponding to each candidate packaging scheme in the candidate packaging scheme set and the coordinate range of material attributes in the material inventory data, and to mark the candidate packaging scheme that passes the intersection determination as a feasible packaging scheme. The production scheduling module is used to insert the feasible packaging scheme into the idle interval of the production timeline based on the idle time period in the production equipment status data, and generate a production scheduling record.

2. The big data-based plastic product packaging management system according to claim 1, characterized in that, The specific method by which the data aggregation module generates a multi-source data index is as follows: The data sources include customer order data, material inventory data, production equipment status data, logistics and transportation data, and environmental temperature and humidity data. The data stream of each data source is divided according to a preset time window to obtain multiple time slices; Attach a data source identifier and timestamp to each time slice, and sort all time slices within the same time window according to the lexicographical order of the data source identifiers; The sorted time slice data records are concatenated into a byte stream, and bit-folding operation is performed on the byte stream to obtain the folded fingerprint; Based on the mapping position of the folded fingerprint on the annular storage slot, a pointer pointing to the time slice is established, and the pointer is stored in the multi-source data index.

3. The big data-based plastic product packaging management system according to claim 2, characterized in that, The requirement parsing module generates the candidate packaging scheme set in the following way: Extract the length, width, height, load-bearing capacity, and material code from the packaging specification parameters in the time slice; The length, width, height, load-bearing capacity, and material code are mapped onto the five coordinate axes of the packaging scheme space to obtain the initial coordinate points; Expand the coordinates around the initial coordinate point by one step in each positive and negative direction of each coordinate axis to form a coordinate set containing multiple expanded coordinate points; All coordinate points in the coordinate set are paired up, and each pair generates a candidate packaging scheme, thus obtaining the candidate packaging scheme set.

4. The big data-based plastic product packaging management system according to claim 1, characterized in that, The collision detection module performs intersection determination in the following way: Read the coordinate range corresponding to the candidate packaging scheme, and read the upper and lower boundary values ​​of the coordinate range of each material attribute in the material inventory data; Compare the coordinate range with the upper and lower boundary values ​​to determine whether the coordinate range is completely within the material property coordinate range. If the contents are completely within the specified range, the intersection is considered successful, and the corresponding candidate packaging scheme is marked as a feasible packaging scheme; otherwise, the candidate packaging scheme is discarded.

5. A big data-based plastic product packaging management system according to claim 1, characterized in that, The production scheduling module generates production schedule records in the following way: The idle time periods of each production device are extracted from the production equipment status data and arranged in chronological order of their start times to establish a production timeline. Read the production equipment identification and estimated production time for each of the feasible packaging solutions; Locate the idle interval corresponding to the production equipment identifier on the production timeline, and determine whether the length of the idle interval is not less than the expected production time; If there exists a free interval with a minimum length that is not less than the estimated production time, then the feasible packaging solution is placed in that free interval, and the production scheduling record is updated.

6. The big data-based plastic product packaging management system according to claim 1, characterized in that, The system also includes: The quality traceability module is used to collect the actual packaging parameters of each finished product during the packaging production process, combine the actual packaging parameters with the identification code of the corresponding feasible packaging scheme to generate a traceability code, and send the traceability code to the distributed storage node; The quality traceability module generates traceability codes in the following way: Each actual value in the actual packaging parameters is encoded and compressed to obtain a parameter encoding string; The parameter encoding string is concatenated with the identifier of the feasible packaging scheme to obtain the concatenated string; Perform cyclic redundancy check on the concatenated string to generate a checksum; The parameter encoding string, the identifier code, and the check code are combined in a predetermined order to form the traceability code, and the traceability code is associated with the production timestamp and written into the distributed storage node.

7. A big data-based plastic product packaging management system according to claim 6, characterized in that, The system also includes: The logistics scheduling module is used to obtain the traceability code from the distributed storage node and generate a loading sequence based on the destination code and packaging size in the traceability code; The logistics scheduling module generates the loading sequence in the following way: Extract the destination code, packaging size, and packaging weight from the traceability code; Read the remaining volume, remaining load, and arrival time window of the transport vehicles from the logistics and transportation data; Calculate the volume occupancy of each packaged product based on the package dimensions, and calculate the weight occupancy of each packaged product based on the package weight; The volume and weight occupancy values ​​of multiple packaged finished products are combined and matched according to the remaining volume and remaining load capacity of the transport vehicle to obtain multiple loading candidate combinations; The multiple loading candidate combinations are sorted according to the order of arrival time windows of the transport vehicles to generate the loading sequence.

8. A big data-based plastic product packaging management system according to claim 2, characterized in that, In the data aggregation module, the lexicographical order is arranged according to the alphabetical order of the data source name, and after sorting, a sequential number is assigned to each time slice. The sequential number and the folded fingerprint are used together as the mapping key value of the ring storage slot.

9. A big data-based plastic product packaging management system according to claim 3, characterized in that, In the requirement analysis module, the step size is dynamically determined based on the material shrinkage rate corresponding to the material code. Different material codes correspond to different step size values, and the step size value increases as the material shrinkage rate increases.

10. A big data-based plastic product packaging management system according to claim 6, characterized in that, In the quality traceability module, the actual packaging parameters include actual length, actual width, actual height, and actual load-bearing capacity. When encoding and compressing each actual value, differential encoding is used. The difference is calculated and stored based on the corresponding standard parameter value in the feasible packaging scheme.

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