Bar code data processing method and system

Abstract

The present application provides a bar code data processing method and system, and relates to the technical field of information processing.The present application obtains a product bar code image, analyzes to obtain product identification information and basic data, evaluates to obtain bar code reading quality, and constructs a bar code data object in combination with environmental state data; determines a detection task according to the basic data, calculates environmental compensation data and writes the environmental compensation data into the object; sends the task to a detection device and receives returned detection data, determines a data processing strategy according to the bar code reading quality, thereby controlling the writing and updating mode of the detection data; and finally, the detection data is standardized and verified by using the environmental compensation data, and a detection result is generated and updated to the bar code data object.The present application can effectively eliminate environmental interference and prevent data misbinding caused by abnormal bar codes, and improves the rigor of the detection result and the consistency of data traceability.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, specifically to a barcode data processing method and system. Background Technology

[0002] In real industrial testing workshops, data management systems and underlying testing equipment have not yet achieved deep integration. Current software and hardware interactions often remain at the level of simple command issuance and result recording, ignoring the impact of dynamic environmental fluctuations on underlying test data, and failing to incorporate the real-time status of physical tags into business logic, making it difficult to accurately reproduce and solidify the true quality attributes of physical objects.

[0003] In existing barcode data processing, an architecture combining industrial PCs and PLCs is commonly used for data processing and production line control. For example, in one data acquisition and processing method, an external barcode reader simply reads the barcode and determines success or failure, transmitting the data to a terminal buffer. Simultaneously, a monitoring device physically monitors the product, sending pass / fail records to the terminal. At the end of the process, the system compares the barcode data with the monitoring records to verify the product. If the verification fails, the PLC-controlled removal device rejects the defective product.

[0004] However, this technology has significant limitations when dealing with complex industrial scenarios. First, the data triggering mechanism is too rigid, easily leading to risks in data transfer. Existing technology relies on simple binary judgments, which cannot accurately identify barcodes that are blurry or damaged and in a marginally readable state. This easily leads to mis-binding of underlying data with the physical identity of the item, causing subsequent data transfer chaos. Second, the data judgment logic is too isolated, making it difficult to guarantee the rigor of the conclusions. The existing architecture mechanically records only the single result output by the monitoring equipment, severing the physical coupling between the detection values ​​and the real-time testing environment. The system only performs static comparisons of the raw values, ignoring the comprehensive impact of environmental fluctuations and the product's own attributes on the test, resulting in a lack of scientific rigor in the detection results and failing to meet the high consistency traceability requirements of industrial-grade systems.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a barcode data processing method and system to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: Barcode data processing methods and systems, including the following specific steps: Step 1: Acquire the product's barcode image, parse the barcode image to obtain product identification information and basic data; perform quality assessment on the barcode image to obtain barcode reading quality; simultaneously acquire environmental status data of the inspection station; construct a barcode data object based on the product identification information, the basic data, the barcode reading quality, and the environmental status data. Step 2: Determine the corresponding detection task based on the basic data, calculate the environmental compensation data based on the environmental state data and the basic data, and write the environmental compensation data into the barcode data object; Step 3: Send the detection task to the corresponding target detection device, receive the detection data returned by the target detection device, determine the data processing strategy of the barcode data object according to the barcode reading quality, and control the writing and updating methods of the detection data according to the data processing strategy; Step 4: Standardize the detection data according to the environmental compensation data to obtain standardized detection data, verify the standardized detection data, generate detection results, and update the detection results to the barcode data object.

[0008] Furthermore, the basic data includes product material information, product category information, and implementation standard information.

[0009] Furthermore, the quality assessment of the barcode image includes: extracting the barcode symbol contrast, edge sharpness, and percentage of soiled area of ​​the barcode image, and calculating the barcode reading quality based on the barcode symbol contrast, edge sharpness, and percentage of soiled area. Using the product identification information as a unique index, the basic data, the barcode reading quality, and the environmental status data are written into the corresponding data fields of the preset data structure template to construct the corresponding barcode data object.

[0010] Furthermore, the barcode data object includes a basic information layer, an environmental information layer, a detection information layer, and a status information layer; The basic information layer is used to store product identification information and basic data; The environmental information layer is used to store environmental status data and environmental compensation data; The detection information layer is used to store detection data and standardized detection data; The status information layer is used to store detection results, update time, and detection status.

[0011] Furthermore, based on the material information, the product category information, and the execution standard information, and according to a preset rule base, the testing items and testing order are determined, and the testing items are arranged according to the testing order to obtain the testing task; The material information is analyzed to obtain the environmental sensitivity coefficient, and the environmental deviation between the environmental state data and the standard test environment is calculated. Using the environmental sensitivity coefficient as the weight, the environmental deviation is weighted and calculated to obtain the environmental compensation factor. The environmental deviation amount and the environmental compensation factor are encapsulated into environmental compensation data, and the environmental compensation data is written into the barcode data object as an environmental attribute of the barcode data object.

[0012] Furthermore, a corresponding task identifier is generated based on the detection task, and the task identifier is bound to the product identifier information; The target detection device is controlled to perform the corresponding detection according to the detection task, and the detection data carrying the task identifier is transmitted back. The correspondence between the detection data and the barcode data object is verified according to the task identifier. If the verification matches, the write operation of the detection data is allowed. If the verification does not match, the detection data is blocked.

[0013] Furthermore, the barcode reading quality is compared with a preset quality threshold; The barcode reading quality is compared with the quality threshold. When the barcode reading quality is not lower than the preset quality threshold, the direct writing strategy is adopted, and the received detection data is directly written to the corresponding barcode data object. When the barcode reading quality is lower than the quality threshold, an enhanced verification transmission strategy is adopted. The received detection data is temporarily stored in the unconfirmed data area of ​​the barcode data object. A confirmation instruction is received, and the identity consistency of the detection data in the unconfirmed data area is confirmed. After the confirmation is successful, the detection data is updated to the detection information layer, and the detection status is updated to the confirmed status. If the detection fails, the detection data is marked as abnormal data, and the detection status remains as pending confirmation.

[0014] Furthermore, the detection data is reverse-calculated based on the environmental compensation data to obtain standardized detection data; The execution standard information and the product category information are jointly analyzed to obtain the legal constraint indicators for the testing task; The standardized test data is verified against the legally binding constraints to obtain test results, which include qualified data and unqualified data. Based on the product identification information, locate the corresponding barcode data object, write the standardized test data, the test result and the corresponding update time into the barcode data object, and update the test status of the barcode data object.

[0015] A barcode data processing system, wherein the barcode data processing system is used in the above-mentioned barcode data processing method, comprising: The object construction module is used to acquire the barcode image of the product, parse the barcode image to obtain product identification information and basic data; perform quality assessment on the barcode image to obtain barcode reading quality; synchronously acquire environmental status data of the detection station; and construct a barcode data object based on the product identification information, the basic data, the barcode reading quality, and the environmental status data. The environmental compensation module is used to determine the corresponding detection task based on the basic data, calculate environmental compensation data based on the environmental state data and the basic data, and write the environmental compensation data into the barcode data object. The strategy writing module is used to send the detection task to the corresponding target detection device, receive the detection data returned by the target detection device, determine the data processing strategy of the barcode data object according to the barcode reading quality, and control the writing and updating methods of the detection data according to the data processing strategy. The result determination module is used to standardize the detection data based on the environmental compensation data to obtain standardized detection data, verify the standardized detection data, generate detection results, and update the detection results to the barcode data object.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention obtains product identification information and basic data by parsing barcode images, evaluates barcode reading quality, and constructs a barcode data object by combining synchronously acquired environmental status data. When receiving detection data returned by the target detection device, this invention breaks away from existing technologies that rely solely on binary judgment, directly using barcode reading quality as the control basis for data processing strategies. It can adaptively match differentiated data writing and update processes for barcodes with different clarity and quality states. This method achieves refined identification and dynamic control of abnormal reading scenarios, completely intercepting the risk of data misbinding caused by barcode wear and deterioration in industrial settings, ensuring the absolute accuracy of the correspondence between product entities and digital archives. This invention also determines the testing task through basic data, calculates specific environmental compensation data by combining environmental state data, and writes it into the barcode data object. Before performing verification judgment, the original test data is standardized and reverse-calculated using this environmental compensation data. Existing technologies often mechanically perform static comparisons of original test values, severing the physical coupling relationship between test values ​​and the test environment. This invention incorporates real-time environmental fluctuations into the underlying control logic, successfully eliminating physical measurement errors caused by dynamic temperature and humidity fluctuations on site, accurately restoring and solidifying the true physical properties of the product. This fundamentally makes up for the shortcomings of traditional judgment logic being too isolated, significantly improving the scientific rigor of the final test conclusion and the consistency of the entire traceability chain. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall method flow of the present invention.

[0018] Figure 2 This is a distribution diagram of the environmental compensation factors of this invention.

[0019] Figure 3 This is a block diagram of the overall device module of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] Example: Please see Figures 1-2 The present invention provides a technical solution: Barcode data processing methods, including the following steps: Step 1: Acquire the product's barcode image, parse the barcode image to obtain product identification information and basic data; perform quality assessment on the barcode image to obtain barcode reading quality; simultaneously acquire environmental status data of the inspection station; construct a barcode data object based on the product identification information, the basic data, the barcode reading quality, and the environmental status data.

[0023] In this embodiment, the basic data includes the product's material information, product category information, and implementation standard information.

[0024] In the context of digital product passport applications for textiles, after acquiring a barcode and parsing the product identification information, the control platform accurately retrieves the corresponding basic data dictionary from the underlying database based on this identification information. To achieve subsequent high-precision environmental compensation and verification, the basic data includes: Material information: Includes the specific fiber composition, ratio, and microscopic physical specifications of the sample to be tested. The material information is set as "high-strength polyester fiber, 100% blend, yarn count 150D / 48F".

[0025] Product Category Information: Clearly define the final business application and safety access level of the fabric. The product category information is set as "Industrial Textiles - Special High-Altitude Operation Protective Belt, End-of-Life Safety Level A".

[0026] Execution Standard Information: Extract the code of the legal or industry standard document that must be followed for the test. The execution standard information is set to "GB / T3923.1-2013".

[0027] In traditional textile testing processes, barcodes are often simply linked to an execution standard code. This crude data management approach leads to difficulties in subsequent testing and judgment. This design mandates a three-dimensional binding of material, category, and standard. While the execution standard merely specifies how the machine should stretch the fabric, materials with different microscopic dimensions exhibit drastically different deformation responses to tensile testing. Furthermore, even fabrics of the same material have different quality acceptance thresholds when used for ordinary bags versus high-altitude work slings. By collecting these three fundamental data points and performing cross-logic operations, the control module can tailor the physical behavior boundaries for the actual product before it is put into machine testing.

[0028] In this embodiment, the quality assessment of the barcode image specifically includes: extracting the barcode symbol contrast, edge sharpness, and percentage of soiled area of ​​the barcode image, and calculating the barcode reading quality based on the barcode symbol contrast, edge sharpness, and percentage of soiled area.

[0029] Based on the actual lighting and dust conditions in the textile workshop, after image acquisition, the maximum and minimum reflectance of the barcode area are first obtained through grayscale histogram analysis. The barcode symbol contrast is then obtained by subtracting these two values. Simultaneously, by traversing the pixel grayscale change rate at the boundary between black and white bars and spaces in the barcode, the average gradient value of the edge transition pixels is extracted as the edge sharpness. Morphological filtering is used to identify irregular ink stains or oil stains within the barcode area, and the total number of pixels in these stains is divided by the total number of pixels in the barcode to obtain the percentage of soiled area. The formula for calculating barcode reading quality is:

[0030] In the formula, For barcode reading quality, This is the contrast weighting coefficient. For barcode symbol contrast, For edge sharpness, As a penalty factor for the soiling index, This represents the percentage of the area affected by soiling.

[0031] The system quantifies the physical reliability of barcode images in complex industrial workshops, where factors such as fluctuating lighting, dust adhesion, and oil splatter exist, ensuring accurate and stable decoding by underlying scanning devices. As the core trigger parameter of the state machine, it directly determines the subsequent data processing strategy: either high-scoring data is trusted and directly written, or low-scoring data is intercepted and isolated for verification. This completely intercepts data errors and misbinding risks caused by edge-quality barcodes from the software level, ensuring the absolute accuracy of digital archive traceability.

[0032] The threshold determines whether the underlying algorithm can effectively extract the binarization threshold, which is the cornerstone of recognition. Therefore, as a linear product term, it directly affects the global score.

[0033] This determines whether the width ratio of bars to spaces can be accurately measured. This is especially critical for high-density industrial barcodes. This embodiment applies a square root nonlinear transformation to edge sharpness to reduce the impact of local amplitude disturbances caused by image acquisition noise after edge feature extraction on the quality evaluation results. The square root nonlinear transformation is not used to replace spatial domain filtering of the original barcode image, but rather to nonlinearly compress the impact of noise disturbances transmitted to the edge sharpness evaluation index, maintaining the monotonic distinguishability of edge sharpness while reducing the excessive influence of abnormally high gradients on the quality score.

[0034] The percentage of contaminated area represents the decoding reliability. When the contaminated area is within the redundancy limit, high decoding reliability can be maintained within the preset error correction capability range. However, once the error correction threshold is exceeded, the decoding capability will collapse. Therefore, the formula introduces an exponential decay function with a natural base to characterize the nonlinear decay relationship of effective decoding capability as the degree of contamination increases.

[0035] and There is a positive correlation; the greater the difference between the black and white reflections of the barcode, the higher the contrast, and the higher the barcode reading quality score. The higher the value, and The results show a non-linear positive correlation; the sharper and clearer the edges, the higher the score. By compressing the amplitude of edge sharpness through a square root non-linear transformation, the quality score gain brought about by improved edge sharpness gradually slows down, thereby reducing the excessive influence of local high gradient anomalies on the overall quality score.

[0036] and There is an exponential negative correlation; the larger the proportion of soiled area, the lower the barcode reading quality score. The lower the value, the better. Furthermore, due to the use of a negative exponential model, when the proportion of soiled area is small, the score decreases more slowly, reflecting the barcode's fault tolerance; as the proportion of soiled area increases, the quality score continuously decreases according to an exponential decay relationship; the larger the soiled area penalty factor, the more sensitive the quality score is to changes in the proportion of soiled area. The contrast weight coefficient is set to a range of [0.8, 2.5]. In a standard textile lighting workshop using mainstream industrial CMOS barcode scanners, this embodiment sets it to 1.2. The contrast weight coefficient is determined during system initialization through a hardware sensitivity calibration program.

[0037] The soiling index penalty factor is set to a range of [3.0, 10.0]. For Class A special protective textiles, the value is 5.0; for ordinary civilian textiles, the value can be lowered to 3.5. The soiling index penalty factor represents the sensitivity to tolerance limits for defects such as physical damage to barcodes and ink contamination. Its technical effect is to control the degree of skewness in quality scores. It allows for flexible setting of the stringency of intercepting dirty barcodes based on the traceability security level of different products.

[0038] Conventional barcode evaluation typically uses a simple weighted sum of scores from various components. However, in textile dyeing and finishing or weaving workshops, fly ash, oil stains, or moisture often cause severe localized damage to barcodes. The formula above introduces a natural base to construct an exponential decay function, simulating the physical phenomenon of fault tolerance collapse in the underlying decoding logic of optical barcode scanners—when the percentage of damage is within the error correction limit of the error-correcting code, the score decreases slowly; once the damage exceeds the critical point, the quality score is forcibly suppressed by the exponential term. This calculation method provides a priori criterion for whether subsequent steps allow automatic writing of underlying data.

[0039] Using the product identification information as a unique index, the basic data, the barcode reading quality, and the environmental status data are written into the corresponding data fields of the preset data structure template to construct the corresponding barcode data object.

[0040] After completing the quality assessment, the parsed product identification information is used as the primary key record, i.e., a unique index, to call a preset JSON data structure template in the background relational database. Strictly following the key-value pair mapping rules, the read basic data, the calculated barcode reading quality, and the environmental status data synchronized through the sensor serial port are written into the corresponding isolated fields within the JSON template, thereby instantiating a completely isolated and information-complete barcode data object in memory.

[0041] In textile testing scenarios, thousands of batches of fabrics circulate daily, and even subtle changes in ambient temperature and humidity can directly interfere with the tensile strength of polyester fibers. By strongly binding environmental condition data, barcode reading quality, and basic data into a single data object using a unique DPP traceability code, data misalignment during subsequent multi-threaded concurrent testing is less likely to occur. Using product identification information as a unique index to construct a structured template eliminates the risk of dirty reads during concurrent data writing, improving the high-precision calculation and traceability of textile testing indicators.

[0042] In this embodiment, the barcode data object includes a basic information layer, an environmental information layer, a detection information layer, and a status information layer; The basic information layer is used to store product identification information and basic data; The environmental information layer is used to store environmental status data and environmental compensation data; The detection information layer is used to store detection data and standardized detection data; The status information layer is used to store detection results, update time, and detection status.

[0043] In this embodiment, considering the digital product passport business scenario for textiles, the barcode data object is strictly divided into four independent storage layers in memory, each with a logically sequential relationship. A detailed explanation follows: Basic Information Layer: Serving as the anchor point for static physical files, this layer pre-sets and solidifies the fabric's factory attributes. It specifically includes product identification information and basic data, such as material information "high-strength polyester fiber 150D," product category "special high-altitude work protective belt," and implementation standard "GB / T3923.1-2013."

[0044] Environmental Information Layer: Used to capture slices of the physical space at the time of the test. Specifically, it stores environmental state data collected by the sensors connected to the grid, such as real-time room temperature of 23.5°C, relative humidity of 68%, and environmental compensation data.

[0045] The detection information layer serves as the core buffer for underlying hardware interaction, isolating and storing dynamic, measured business data. Specifically, it writes raw test data directly transmitted from the tensile testing machine's serial port, such as the raw breaking strength value of 815N directly output from the force sensor, and standardized test data output after being converted using the aforementioned environmental compensation factor, such as the calculated true baseline strength value of 849.2N after excluding interference from the current high temperature and humidity environment.

[0046] Status Information Layer: Records the final transfer conclusion and lifecycle nodes of the current barcode object. Specifically, it stores the detection results derived from the comprehensive judgment model of the storage system, such as comparing with the 840N preset pass limit, writing the judgment result as "pass", a timestamp, such as the update time accurate to milliseconds, and the detection status of the current process.

[0047] In product quality inspection management, data is often simply and flatly piled up in a single two-dimensional database table. This storage mechanism has a fatal flaw when dealing with stringent textile testing and traceability: once it is necessary to review a quality dispute of a batch of fabric, it is impossible to distinguish from the form whether a certain tensile value is the original value directly measured by the underlying hardware, or a value that has been adjusted by environmental factors or artificially modified.

[0048] By forcibly splitting barcode data objects into four independent logical layers—"Foundation, Environment, Detection, and State"—its core significance in software engineering and industrial control theory lies in constructing a firewall. The Foundation layer represents the product itself, the Environment layer represents external interference during testing, the Detection layer represents physical interaction behavior, and the State layer represents decision-making conclusions. These four layers are strictly unidirectionally recursive in terms of timestamps and logical causality. In particular, storing raw sensor data and standardized, converted data side-by-side in the detection information layer without direct overwriting achieves zero-intrusion retention of the underlying hardware's raw test data. This highly cohesive and loosely coupled layered architecture design not only improves memory addressing and tracking efficiency during massive concurrent barcode scanning and detection but also endows textile digital product passport data with extremely high traceability and anti-counterfeiting levels, fundamentally ensuring the objectivity, self-consistency, and immutability of the textile testing and circulation data chain.

[0049] Step 2: Determine the corresponding detection task based on the basic data, calculate the environmental compensation data based on the environmental state data and the basic data, and write the environmental compensation data into the barcode data object.

[0050] In this embodiment, based on the material information, the product category information, and the execution standard information, and according to a preset rule base, the testing items and the testing order are determined, and the testing items are arranged according to the testing order to obtain the testing task.

[0051] For the digital product passport service for textiles, the control center first reads three basic data points obtained in previous steps: material information as "high-strength polyester fiber," product category as "special high-altitude work protective belt," and implementation standard as "GB / T3923.1-2013." These three data points are then input into a preset rule base for joint mapping to match the set of mandatory inspection items. The order of these items is determined by time: non-destructive tests such as apparent surface density measurement and basis weight determination are placed at the top of the queue; tests requiring sample wetting, such as "water absorption rate test," are placed in the middle; and tests involving complete destructive testing, such as "strip tensile strength test," are placed at the bottom. Finally, the ordered queues are packaged into structured inspection tasks and distributed to the corresponding testing stations.

[0052] The pre-defined rule base is constructed using the standard code library as the core key. All mandatory inspection items explicitly stipulated in legal documents or industry standards are extracted and written into the underlying relational database as the initial dataset, forming a benchmark testing outline. Terminal application scenarios and security risk levels for different product categories are entered, and product category tags are cross-bound with the benchmark testing outline. For special or high-security categories, specific working condition test items are forcibly added under the category mapping through logical operations, generating a joint test set. The physicochemical properties of various basic materials are entered, forming an exclusive logical discrimination tree. When the joint test set circulates within the library, the discrimination tree automatically compares and filters out invalid test items that conflict with the current material's physical properties, ensuring that the test items absolutely match the physical object. A digital physical damage coefficient is preset for each test item retained in the library; for example, the preset coefficient is 0 for non-destructive items, 1 for slightly damaged items, and 2 for irreversible damage items. A sorting algorithm is forcibly introduced into the library structure to sort items in ascending order based on their physical damage coefficient. The sorted list of items is then encapsulated and solidified into the final output template of the rule library.

[0053] In real industrial testing workshops, fabric samples undergoing testing based on a single barcode are often unique and limited in number. If testing items are simply issued without a mandatory physical execution order, and testing personnel or automated robotic arms prioritize tensile failure tests, the originally intact fabric will be torn and shredded, making subsequent non-destructive measurements such as dimensions, weight, and color difference impossible. This would halt the entire inspection process and render the samples unusable. This step, through multi-dimensional basic data mapping and the mandatory establishment of a "non-destructive first, minor damage in the middle, and destructive last" sequence list, eliminates irreversible conflicts between multiple testing processes from the very source of physical operation logic.

[0054] The material information is analyzed to obtain an environmental sensitivity coefficient, and the environmental deviation between the environmental state data and the standard test environment is calculated.

[0055] First, the high-strength polyester fiber material information is analyzed using polymer physical properties. Preset environmental sensitivity coefficients are retrieved from the material library: a temperature sensitivity coefficient of 0.015 and a humidity sensitivity coefficient of 0.035. Then, environmental status data synchronously acquired by the sensors is read, for example, the current actual room temperature in the workshop is 26℃ and the actual relative humidity is 75%. The absolute calibration environment specified by national standards is also retrieved, with a preset standard temperature of 20℃ and a preset standard humidity of 65%. The environmental deviation of the current detection station is directly calculated using basic difference logic, i.e., an absolute temperature deviation of 6℃ and an absolute humidity deviation of 10%.

[0056] Different materials exhibit vastly different resistance to environmental fluctuations. For example, natural cotton fibers react extremely strongly to humidity, while polyester fibers, as hydrophobic polymers, are relatively less sensitive to humidity but more sensitive to temperature changes. Without in-depth material property analysis, directly applying uniform environmental compensation parameters will lead to severe distortion of subsequent calculated data. Calculating precise temperature and humidity deviations is crucial to eliminating pure noise from the physical testing sensors, providing the cleanest underlying operators for subsequent high-order environmental compensation algorithms.

[0057] Using the environmental sensitivity coefficient as a weight, the environmental deviation is weighted and calculated to obtain the environmental compensation factor.

[0058] The formula for calculating the environmental compensation factor is:

[0059] In the formula, As an environmental compensation factor, For temperature sensitivity coefficient, This is the absolute temperature deviation. To preset standard temperature, Humidity sensitivity coefficient This is the absolute deviation of humidity. To preset standard humidity, This is the operator for the natural logarithm.

[0060] The environmental compensation factor specifically reflects the quantified value of the comprehensive interference caused to the physical properties of the target material when the actual environment of the current testing station deviates from the national or industry standard testing environment. In real industrial workshops, because it is impossible to maintain absolutely constant temperature and humidity at all times, the raw test data output by the underlying testing equipment often carries environmental bias. By introducing this factor, in subsequent data flow, the environmentally affected raw test data can be reverse-calculated and projected to restore the true physical properties of the product under standard conditions. This not only eliminates the risk of misjudgment caused by dynamic environmental fluctuations but also improves the scientific rigor of the underlying testing data when used for digital product passport traceability.

[0061] The intensity of thermal environmental deviation was measured. A 1.5-power nonlinear design was employed to characterize the nonlinear increasing relationship of environmental impact as the normalized temperature deviation increases. This was achieved by adjusting the temperature sensitivity coefficient. This can further adjust the contribution of temperature deviation to environmental compensation factors.

[0062] The deviation intensity of the water vapor environment was measured. A logarithmic function was constructed by introducing the natural base for this term. This function is used to convert normalized humidity deviation into an environmental impact quantity that increases monotonically with gradually decreasing marginal increments. As humidity deviation increases, this function maintains an increase in the compensation amount while preventing the compensation amount from growing rapidly and indefinitely with the humidity deviation. Humidity sensitivity coefficient. Used to adjust the contribution of humidity to environmental compensation factors.

[0063] Absolute deviation from temperature The correlation is non-linear and positive; the greater the absolute deviation of the actual temperature from the standard temperature, the stronger the interference with the test data, and the greater the compensation factor. The larger the value, the greater the positive correlation. Due to the existence of the 1.5 power, this positive correlation increases exponentially.

[0064] Absolute deviation from humidity The coefficient of performance exhibits a logarithmic positive correlation; the greater the absolute deviation of the actual humidity from the standard humidity, the larger the compensation factor. However, constrained by the natural logarithmic function, it prevents technical distortions caused by overcorrection of the compensation parameters in extremely high humidity workshops.

[0065] When the environmental deviation is fixed, the larger the temperature sensitivity coefficient or humidity sensitivity coefficient of the material itself, the weaker the material's resistance to the environment. The calculated overall environmental compensation factor... Therefore, it increases proportionally.

[0066] The mechanical properties of polymer textile materials are typically affected by ambient temperature and humidity in a nonlinear manner. Therefore, this embodiment uses nonlinear functions to characterize temperature and humidity deviations. A 1.5-power exponent design is used in the temperature weighting term because the activity of macromolecular chain segments in polyester fibers increases nonlinearly with increasing temperature within the normal temperature range. A logarithmic function is introduced in the humidity weighting term. When ambient moisture initially increases, the free water absorption points in the amorphous regions of the fiber rapidly absorb water molecules, acting as a plasticizer and causing a sharp decrease in strength. However, once moisture absorption reaches a certain saturation level, further increases in humidity result in a significant marginal decrease in the degradation of mechanical properties. This white-box nonlinear compensation mechanism prevents the technical risk of overcorrection of compensation parameters in extremely high-temperature and high-humidity workshops.

[0067] The environmental deviation amount and the environmental compensation factor are encapsulated into environmental compensation data, and the environmental compensation data is written into the barcode data object as an environmental attribute of the barcode data object.

[0068] Temperature deviation, humidity deviation, and environmental compensation factor are packaged into a standard JSON data packet to form complete environmental compensation data. Subsequently, using the unique traceability code of the textile DPP as the addressing credential, this JSON data packet is injected into the "environmental information layer" of the barcode data object, solidifying it as an unchangeable environmental slice attribute for this fabric batch.

[0069] If only the final converted test results are saved, traceability will be impossible in case of textile quality inspection disputes or downstream customers questioning the strong data on the DPP passport. By packaging the deviation amount and compensation factor together and writing them independently into the barcode object's dedicated environmental information layer, it is equivalent to preserving complete on-site evidence of this test.

[0070] Step 3: Send the detection task to the corresponding target detection device, receive the detection data returned by the target detection device, determine the data processing strategy of the barcode data object based on the barcode reading quality, and control the writing and updating methods of the detection data according to the data processing strategy.

[0071] In this embodiment, a corresponding task identifier is generated based on the detection task, and the task identifier is bound to the product identifier information.

[0072] For each specific testing task, a globally unique task identifier is first generated. This task identifier is composed of a string representing a timestamp, task type, and serial number. Subsequently, a key-value mapping operation is performed in the activity state machine, using the newly generated task identifier as the key to establish a mandatory binding relationship with the product identification information of the fabric to be tested, i.e., the parsed unique traceability code of the textile DPP, and this mapping instruction is pushed into the cache queue to await a response.

[0073] In textile testing workshops with extremely high concurrency, multiple testing devices often cross-test different batches of fabric, or the same batch of fabric needs to be re-tested multiple times within a short period. If only the product identifier on the barcode itself is used to schedule and receive data, the underlying data flow is prone to overwriting and corruption when business overlaps. By generating a unique, non-reusable task identifier for each independent physical testing action and forcibly binding it to the product identity, a basic anchor point is established for accurate addressing and identity verification of subsequent data.

[0074] The target detection device is controlled to perform the corresponding detection according to the detection task, and the detection data carrying the task identifier is transmitted back.

[0075] The test execution message is sent to the corresponding target detection device via the industrial communication bus. After receiving the tagged instruction, the target detection device activates the underlying servo mechanism and sensors to perform physical damage or non-destructive testing of the fabric. After the physical test is completed and peak or steady-state data is collected, the received task tag and the measured detection data are combined and encapsulated into a structured communication data packet. The data packet carrying the tag watermark is then transmitted back to the control center.

[0076] If network message delays occur, or if on-site operators make operational errors, the receiving end will completely lose the ability to identify the data's origin. The received task identifier will be returned intact.

[0077] The correspondence between the detection data and the barcode data object is verified according to the task identifier. If the verification matches, the write operation of the detection data is allowed. If the verification does not match, the detection data is blocked.

[0078] After capturing the data packet returned by the target detection device, the string parsing logic is first invoked to extract the task identifier carried in the packet header. Then, a precise comparison is performed in the memory-mapped table to check if the task identifier matches the barcode data object currently waiting to be received. If the logical comparison confirms a match, the data source is deemed legitimate, and the write lock on the underlying database is released, allowing the extracted detection data to be officially written to disk and enter the detection information layer of the barcode data object. If the comparison does not match, for example, if the data packet carries a historical task identifier that has been cancelled, or an identifier belonging to another concurrent process, a data interception barrier will be immediately triggered, directly discarding the data packet at the gateway layer and triggering an abnormal state interruption, prompting manual intervention to investigate on-site sample errors.

[0079] In the industrial IoT architecture, network jitter causing out-of-order messages or non-standard operations by on-site personnel can lead to catastrophic test result errors. By verifying task identifiers through a strict interception and release logic, the control center abandons unconditional trust in data transmitted back from underlying devices. This prevents asynchronous dirty data from contaminating digital product passport files, fundamentally improving the high confidence and traceability rigor of textile industry quality data.

[0080] In this embodiment, the barcode reading quality is compared with a preset quality threshold.

[0081] The barcode reading quality is compared with the quality threshold. When the barcode reading quality is not lower than the preset quality threshold, a direct writing strategy is adopted, and the received detection data is directly written to the corresponding barcode data object.

[0082] In a quality inspection scenario within a textile production workshop, after receiving inspection data from the underlying equipment, a barcode reading quality score is extracted. This score is then logically compared to a preset quality threshold. This threshold is set based on the hardware error correction limit of the barcode scanner in the workshop and its historical misjudgment rate; for example, a preset quality threshold of 0.80. If the current barcode reading quality score of 0.92 is greater than the preset quality threshold of 0.80, the barcode is determined to be in a high-confidence state, automatically triggering a "direct write strategy." The inspection data is then directly allowed to pass through, seamlessly written to the "inspection information layer" of the barcode data object, quickly completing the data flow and on-chain processing.

[0083] In high-volume industrial inspection, processing efficiency is paramount. Performing rigorous manual verification on every single piece of data would significantly slow down the production line. The core physical logic behind this direct-write mechanism lies in the fact that when the physical characteristics of the optical barcode are excellent, far exceeding the critical point of the error correction algorithm, the physical probability of the barcode scanner outputting an incorrect barcode approaches zero. Based on this highly reliable physical fact, a trust-based release approach is adopted, thereby maximizing the efficiency and smoothness of data acquisition in the industrial field while ensuring absolute data security.

[0084] When the barcode reading quality is lower than the quality threshold, an enhanced verification transmission strategy is adopted. The received detection data is temporarily stored in the unconfirmed data area of ​​the barcode data object. A confirmation instruction is received, and the identity consistency of the detection data in the unconfirmed data area is confirmed. After the confirmation is successful, the detection data is updated to the detection information layer, and the detection status is updated to the confirmed status.

[0085] If the detection fails, the detection data is marked as abnormal data, and the detection status remains as pending confirmation.

[0086] For another batch of textile fabrics that suffered severe oil stains during its transfer in the dyeing and finishing workshop, its barcode reading quality score, as assessed by the initial algorithm, was only 0.65. Comparing this score to a preset quality threshold, it was determined that the current score was below the threshold. At this point, the state machine of the control center automatically transitioned, triggering the "enhanced verification transmission strategy." To ensure the execution of this strategy, a temporary isolation sandbox called the "data to be confirmed area" was created within the memory structure of the barcode data object. The detection data about to be written to disk was forcibly intercepted, preventing it from entering the core "detection information layer," and instead redirected and locked within this isolation sandbox, remaining in a suspended state.

[0087] In the real-world environment of a textile factory, barcodes often become extremely blurry due to dye splatter or severe wear. When faced with barcodes of this poor quality, the underlying barcode scanners often forcibly activate extreme error correction algorithms to guess the code. This mechanism can easily result in a scanned code that is valid but not the actual code for the current fabric. By judging the physical image quality, once quality deterioration is detected, the data is immediately isolated in a sandbox, cutting off the path for misaligned and dirty data to contaminate the core database from the bottom layer of the architecture.

[0088] For the test data isolated in the "data to be confirmed" area, an anomaly verification alarm is issued to the human-machine interface of the testing station or the quality inspector's handheld terminal. Upon receiving the alarm, the quality inspector visually checks the batch number printed on the work order for the fabric, or scans the auxiliary RFID tag on the fabric using radio frequency scanning, and sends an identity consistency confirmation instruction.

[0089] If it is manually confirmed that the current physical object is indeed consistent with the barcode information, the control center will release the sandbox isolation, officially transfer and update the temporarily stored detection data to the "detection information layer", and at the same time change the status flag in the status information layer of the barcode data object to "confirmed status", allowing the fabric to continue to circulate; If the confirmation instruction feedback is inconsistent, the writing link will be completely blocked, the data in the sandbox will be marked with an "abnormal label" and archived as dirty data for future reference, and the current barcode object will be locked in the "pending confirmation state" to force the production line to suspend the flow of the fabric and wait for rescanning or manual resampling.

[0090] For low-quality barcodes identified as high-risk, the direct decision-making power of the underlying automation is stripped away, forcibly introducing a higher-level physical identity verification. This forcibly anchors the isolated digital trust in the software system back to real-world physical trust. If a misread occurs, because the data has been pre-isolated, the genuine product digital passport remains uncontaminated; only after verification can the data be unfrozen and stored. This solves the fatal data misreading problem caused by barcode errors in harsh industrial environments, meeting the stringent bottom-line requirements for the accuracy of traceability data in textile product digital passports.

[0091] Step 4: Standardize the detection data according to the environmental compensation data to obtain standardized detection data, verify the standardized detection data, generate detection results, and update the detection results to the barcode data object.

[0092] In this embodiment, the detection data is reverse-calculated based on the environmental compensation data to obtain standardized detection data.

[0093] First, the raw detection data just transmitted back by the detection equipment is read, and simultaneously, the previously calculated environmental compensation factor is retrieved from the environmental information layer of the barcode data object. The calculation formula for standardized detection data is as follows:

[0094] In the formula, To standardize test data, For detection data, As an environmental compensation factor, This is a correction symbol for environmental deviation. Considering the physical properties of textile materials, if the current workshop is under high temperature and high humidity, the polymer fibers will soften and their strength will decrease, resulting in a lower measured initial tensile strength. A value of +1 is used for positive compensation mapping; conversely, if the environment is extremely dry and cold, causing the material to become brittle and the test value to be artificially high, then... Take -1 and deduct in reverse.

[0095] To standardize the test data, it is not the disturbed physical appearance value directly read by the sensor, but rather a test result that has been reverse-corrected for ambient noise, used to characterize comparable test results under preset standard test conditions. By outputting this absolutely objective standardized value, the impact of ambient temperature and humidity changes on the consistency of the test data is reduced.

[0096] The detection data represents the initial physical appearance under environmental interference. Subsequent compensation and corrections are all scaled proportionally based on this base value.

[0097] This is the environmental compensation factor, which determines the absolute extent of correction to the original data. The greater the deviation of the on-site environment from the standard, the larger this factor, indicating a more severe need for compensation or deduction.

[0098] The environmental deviation direction correction symbol is used to determine the overall deviation direction of the test data caused by the actual environment relative to the standard test environment. Its values ​​include positive, negative, and zero. Specifically, when the combined effect of temperature and humidity factors causes the test data to tend to be lower than the test data under the standard test environment, the environmental deviation direction correction symbol takes a positive value; when the combined effect causes the test data to tend to be higher, it takes a negative value; and when the combined effect is within a preset range with no significant deviation, it takes 0.

[0099] The environmental deviation direction corrector is used to indicate the compensation direction, not the magnitude of the environmental deviation; the magnitude of the environmental deviation is continuously characterized by the environmental compensation factor. For complex environments such as high temperature and low humidity, or low temperature and high humidity, where the directions of temperature and humidity changes are inconsistent, the correction direction is not directly determined based on a single temperature or humidity parameter, but rather by a comprehensive judgment based on the environmental impact quantities corresponding to the temperature and humidity factors.

[0100] When the overall environmental influence is insufficient to cause a significant deviation in the test data relative to the standard test environment, the environmental deviation direction correction sign is set to 0; when the test data shows a definite trend of being lower or higher than the standard test environment, it is set to +1 or -1 respectively.

[0101] Under the premise of locking the environmental correction ratio and correction direction, the greater the original physical pulling force directly output by the sensor, the larger the standardized data calculated will inevitably be.

[0102] When the environment is hot and humid and There is a positive correlation. The harsher the environment, the more likely it is to cause... The larger the value, the greater the positive boost to the original data, resulting in larger standardized data.

[0103] When the environment is extremely cold and dry and There is a negative correlation. The more severe the environmental deviation, the more likely it is to lead to... The larger the value, the stronger the reverse deduction of the original inflated data, and the smaller the final standardized data.

[0104] In the daily testing of most manufacturing enterprises, it is extremely difficult to maintain a standard laboratory environment with absolutely constant temperature and humidity due to energy consumption or workshop conditions. If raw data affected by fluctuations in on-site temperature and humidity is used directly, it will inevitably mask the true physical limits of the material. Without changing the harsh testing environment in reality, the problem is to project the biased physical test results from reality back to the theoretical reference environment in a mathematical model.

[0105] The execution standard information and the product category information are jointly analyzed to obtain the legal constraint indicators for the testing task.

[0106] The system reads the execution standard information and product category information from the basic information layer. First, it consults the national standard database to obtain the lower limit of the acceptable tensile strength benchmark for this material under normal use, preset to 800N. Then, it analyzes the product category information and finds that its end-use belongs to the extremely high safety level "Class A protection". Based on this, a stringency escalation mechanism is triggered, stipulating that this category must have a preset 5% safety margin added above the national standard benchmark. Through simple numerical multiplication, the final legal constraint index specific to this testing task is determined to be 840N.

[0107] The same standard testing methods used to inspect a regular industrial packing strap and a safety belt used for high-altitude operations should not have the same passing standards. Mandatory requirements necessitate multi-dimensional joint analysis of implementation standards and product categories, and reverse-engineering safety thresholds based on business applications. This allows for the creation of extremely precise and compliant quality acceptance baselines for similar materials in different application scenarios, thereby meeting the stringent quality control needs of high-end manufacturing for segmented products.

[0108] The standardized test data is verified against the legally binding constraints to obtain test results, which include qualified data and unqualified data.

[0109] The standardized test data is compared with the legally mandated constraint indicators using Boolean logic. If the standardized test data is greater than or equal to the legally mandated constraint indicator, the comparison is successful, and a specific test result is generated: "Qualified Data". If the converted data is lower than 840N, "Unqualified Data" is generated.

[0110] Based on the product identification information, locate the corresponding barcode data object, write the standardized test data, the test result and the corresponding update time into the barcode data object, and update the test status of the barcode data object.

[0111] The data bus uses the unique traceability code of the fabric's digital product passport as the retrieval key to accurately locate the physical barcode data object within a vast memory pool. Subsequently, the calculated standardized test data and results are written to the object's status information layer, and the server's absolute clock module is invoked to generate a timestamp accurate to milliseconds, which is also written. Finally, the control module changes the barcode data object's detection status from "in progress" to "test completed."

[0112] In industrial digital traceability systems, data lacking timestamps and status locks lacks credibility. Precisely tracking data with unique identifiers not only firmly anchors physical inspection results to virtual digital objects, but the update time and status locks also serve to prevent tampering. When fabric leaves the factory and reaches downstream customers, anyone can scan the code to query the product's digital passport file and clearly see that the product passed a specific level of standard testing at the precise millisecond time, using rigorous scientific data that eliminates environmental interference. This provides a high-value quality foundation for manufacturing deliveries.

[0113] Table 1: Comparison charts of this application and prior art under different temperature and humidity scenarios.

[0114] The data in the table shows that, firstly, by introducing an environmental deviation corrector for positive standardization, the impact of environmental deviation on the test data is reduced, effectively mitigating losses from qualified batches being mistakenly rejected. Secondly, in extremely cold or dry environments, the algorithm can accurately identify falsely high test values ​​caused by material brittleness. By reverse subtraction and forcefully stripping away the pseudo-physical appearance, it rigorously intercepts defective products that rely on environmental fluctuations to pass inspection, avoiding quality omissions. Simultaneously, it possesses extremely strict objective boundaries; even under extreme conditions triggering the highest-intensity compensation mechanism, truly defective products still fail to meet standards, with a preset compensation upper limit limiting the environmental compensation range. Finally, the algorithm's underlying logic is highly self-consistent. When in a standard laboratory environment with constant temperature and humidity, the environmental deviation corrector automatically resets to zero, ensuring complete consistency between the original and converted data without any interference. This achieves seamless compatibility and precise quality control across all weather conditions and variable industrial environments.

[0115] Please see Figure 3 The present invention also provides a barcode data processing system, which is used to implement the above-described barcode data processing method, including: The object construction module is used to acquire the barcode image of the product, parse the barcode image to obtain product identification information and basic data; perform quality assessment on the barcode image to obtain barcode reading quality; synchronously acquire environmental status data of the detection station; and construct a barcode data object based on the product identification information, the basic data, the barcode reading quality, and the environmental status data. The environmental compensation module is used to determine the corresponding detection task based on the basic data, calculate environmental compensation data based on the environmental state data and the basic data, and write the environmental compensation data into the barcode data object. The strategy writing module is used to send the detection task to the corresponding target detection device, receive the detection data returned by the target detection device, determine the data processing strategy of the barcode data object according to the barcode reading quality, and control the writing and updating methods of the detection data according to the data processing strategy. The result determination module is used to standardize the detection data based on the environmental compensation data to obtain standardized detection data, verify the standardized detection data, generate detection results, and update the detection results to the barcode data object.

[0116] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0117] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0119] 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 barcode data processing method, characterized in that, The specific steps include: Step 1: Acquire the product's barcode image, parse the barcode image to obtain product identification information and basic data; perform quality assessment on the barcode image to obtain barcode reading quality; simultaneously acquire environmental status data of the inspection station; construct a barcode data object based on the product identification information, the basic data, the barcode reading quality, and the environmental status data. Step 2: Determine the corresponding detection task based on the basic data, calculate the environmental compensation data based on the environmental state data and the basic data, and write the environmental compensation data into the barcode data object; Step 3: Send the detection task to the corresponding target detection device, receive the detection data returned by the target detection device, determine the data processing strategy of the barcode data object according to the barcode reading quality, and control the writing and updating methods of the detection data according to the data processing strategy; Step 4: Standardize the detection data according to the environmental compensation data to obtain standardized detection data, verify the standardized detection data, generate detection results, and update the detection results to the barcode data object.

2. The barcode data processing method according to claim 1, characterized in that: Basic data includes product material information, product category information, and implementation standard information.

3. The barcode data processing method according to claim 1, characterized in that: The quality assessment of the barcode image specifically includes: extracting the barcode symbol contrast, edge sharpness, and percentage of soiled area of ​​the barcode image, and calculating the barcode reading quality based on the barcode symbol contrast, edge sharpness, and percentage of soiled area; Using the product identification information as a unique index, the basic data, the barcode reading quality, and the environmental status data are written into the corresponding data fields of the preset data structure template to construct the corresponding barcode data object.

4. The barcode data processing method according to claim 1, characterized in that: The barcode data object includes a basic information layer, an environmental information layer, a detection information layer, and a status information layer; The basic information layer is used to store product identification information and basic data; The environmental information layer is used to store environmental status data and environmental compensation data; The detection information layer is used to store detection data and standardized detection data; The status information layer is used to store detection results, update time, and detection status.

5. The barcode data processing method according to claim 2, characterized in that: Based on the material information, product category information, and execution standard information, and in accordance with a preset rule base, the testing items and testing order are determined, and the testing items are arranged in the testing order to obtain the testing task; The material information is analyzed to obtain the environmental sensitivity coefficient, and the environmental deviation between the environmental state data and the standard test environment is calculated. Using the environmental sensitivity coefficient as the weight, the environmental deviation is weighted and calculated to obtain the environmental compensation factor. The environmental deviation amount and the environmental compensation factor are encapsulated into environmental compensation data, and the environmental compensation data is written into the barcode data object as an environmental attribute of the barcode data object.

6. The barcode data processing method according to claim 1, characterized in that: A corresponding task identifier is generated based on the detection task, and the task identifier is bound to the product identifier information; The target detection device is controlled to perform corresponding detection according to the detection task, and the detection data carrying the task identifier is transmitted back. The correspondence between the detection data and the barcode data object is verified according to the task identifier. If the verification matches, the write operation of the detection data is allowed. If the verification does not match, the detection data is blocked.

7. The barcode data processing method according to claim 4, characterized in that: The barcode reading quality is compared with a preset quality threshold. The barcode reading quality is compared with the quality threshold. When the barcode reading quality is not lower than the preset quality threshold, the direct writing strategy is adopted, and the received detection data is directly written to the corresponding barcode data object. When the barcode reading quality is lower than the quality threshold, an enhanced verification transmission strategy is adopted. The received detection data is temporarily stored in the unconfirmed data area of ​​the barcode data object. A confirmation instruction is received, and the identity consistency of the detection data in the unconfirmed data area is confirmed. After the confirmation is successful, the detection data is updated to the detection information layer, and the detection status is updated to the confirmed status. If the detection fails, the detection data is marked as abnormal data, and the detection status remains as pending confirmation.

8. The barcode data processing method according to claim 2, characterized in that: Based on the environmental compensation data, the detection data is reverse-calculated to obtain standardized detection data; The execution standard information and the product category information are jointly analyzed to obtain the legal constraint indicators for the testing task; The standardized test data is verified against the legally binding constraints to obtain test results, which include qualified data and unqualified data. Based on the product identification information, locate the corresponding barcode data object, write the standardized test data, the test result and the corresponding update time into the barcode data object, and update the test status of the barcode data object.

9. A barcode data processing system, characterized in that: The barcode data processing system is used to implement the barcode data processing method according to any one of claims 1-8, including: The object construction module is used to acquire the barcode image of the product, parse the barcode image to obtain product identification information and basic data; perform quality assessment on the barcode image to obtain barcode reading quality; synchronously acquire environmental status data of the detection station; and construct a barcode data object based on the product identification information, the basic data, the barcode reading quality, and the environmental status data. The environmental compensation module is used to determine the corresponding detection task based on the basic data, calculate environmental compensation data based on the environmental state data and the basic data, and write the environmental compensation data into the barcode data object. The strategy writing module is used to send the detection task to the corresponding target detection device, receive the detection data returned by the target detection device, determine the data processing strategy of the barcode data object according to the barcode reading quality, and control the writing and updating methods of the detection data according to the data processing strategy. The result determination module is used to standardize the detection data based on the environmental compensation data to obtain standardized detection data, verify the standardized detection data, generate detection results, and update the detection results to the barcode data object.

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