A big data-based production process intelligent control method

Abstract

The application relates to the field of intelligent production control, and discloses a production process intelligent control method based on big data, which comprises the following steps: acquiring wood information and an order number, and determining preset probe rough measurement point position information according to the wood type number and a pre-stored wood image database; the database comprises preset moisture content intervals, ideal moisture content gradient intervals and probe positions corresponding to each wood type number. The probe information is sent to a rough measurement terminal, a rough measurement moisture content is measured by a detection personnel, and the rough measurement moisture content is compared with the preset interval to obtain a first comparison result of initial regulation or re-measurement; if re-measurement is needed, a fine measurement instruction is sent to obtain a fine measurement moisture content, an actual moisture content gradient is determined, and the actual moisture content gradient is compared with the ideal interval to obtain a second comparison result of secondary regulation or qualification determination; initial regulation, fine measurement, secondary regulation or qualification instructions are generated according to the comparison result and are sent to a processing terminal. Therefore, intelligent monitoring and regulation of the wood moisture content are realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent production control, and more specifically to an intelligent control method for production processes based on big data. Background Technology

[0002] Wood is widely used in construction, furniture, and handicrafts, and its processing quality directly affects its stability and reliability during subsequent use. In existing technologies, moisture content, as a key indicator of wood's physical properties, is a core factor determining wood strength, dimensional stability, and preventing defects such as cracking and deformation.

[0003] The following technical problems frequently exist in existing timber production processes:

[0004] First, existing technologies for detecting moisture content in the wood production process are limited and disconnected from the production process, making it impossible to effectively assess and control the uniformity of wood drying, which in turn leads to potential quality problems such as cracking and deformation in the wood leaving the factory.

[0005] Second, existing technologies lack the ability to correlate and self-optimize the quality data throughout the entire process of wood production, which makes it impossible to accurately trace the root cause of quality problems leaving the factory. Furthermore, production process standards cannot be dynamically adjusted based on actual production big data, resulting in the continuous occurrence of similar quality problems and low production efficiency.

[0006] Third, existing technologies for quality control of timber products adopt static and universal factory standards, failing to implement dynamic adaptation and regulation based on the differences in end-use environments. This leads to quality deterioration of products during circulation and use due to environmental mismatch, making it difficult to guarantee delivery reliability. Summary of the Invention

[0007] The summary section of this invention provides a brief overview of the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0008] This invention proposes an intelligent control method for production processes based on big data to solve one or more of the technical problems mentioned in the background section above.

[0009] This invention provides a big data-based intelligent control method for production processes, comprising: acquiring timber information, wherein the timber information includes timber species number and order number; determining preset probe coarse measurement point location information based on the timber species number and a pre-stored timber profile database, wherein the pre-stored timber profile database includes multiple timber species numbers, a preset moisture content range corresponding to each timber species number, an ideal moisture content gradient range, and preset probe coarse measurement point location information.

[0010] The probe coarse measurement point location information is sent to the coarse measurement terminal so that the testing personnel corresponding to the coarse measurement terminal can measure the moisture content of the wood and obtain the coarse moisture content.

[0011] The rough moisture content is received and compared with the preset moisture content range to obtain the first comparison result. The first comparison result is used to characterize the initial adjustment or retesting of the moisture content of the wood.

[0012] If the first comparison result indicates that the moisture content of the wood needs to be retested, then a fine measurement instruction is sent to the corresponding processing terminal so that the testing personnel at the processing terminal can measure the moisture content of the wood and obtain the fine measurement moisture content.

[0013] Receive the precise moisture content and determine the actual moisture content gradient; compare the actual moisture content gradient with the ideal moisture content gradient range to obtain a second comparison result, which is used to characterize whether the wood has undergone secondary regulation or is qualified.

[0014] Based on the first or second comparison result, an initial control instruction, a fine measurement instruction, a secondary control instruction, or a timber qualification instruction is generated and sent to the corresponding processing terminal.

[0015] Optionally, based on the first or second comparison result, an initial control instruction, a fine measurement instruction, a secondary control instruction, or a timber qualification instruction is generated and sent to the corresponding processing terminal, including:

[0016] If the measured moisture content is less than or equal to the lower limit of the preset moisture content range, the wood is determined to be over-dried, and the difference between the measured moisture content and the lower limit of the preset moisture content range is calculated and determined as the first difference. If the first difference is greater than or equal to the first preset value, a first scrap instruction is generated. If the first difference is less than the first preset value, a first humidification treatment instruction is generated.

[0017] If the rough moisture content is greater than or equal to the upper limit of the preset moisture content range, the wood is determined to be not completely dry, and a first and second drying treatment instruction is generated.

[0018] The first scrapping order, the first humidification treatment order, or the first secondary drying treatment order shall be determined as the initial control order.

[0019] If the rough moisture content is within the preset moisture content range, a fine measurement command will be generated.

[0020] Optionally, based on the first or second comparison result, an initial control command, a precision measurement command, a secondary control command, or a timber qualification command is generated and sent to the corresponding processing terminal, and the process further includes:

[0021] If the actual moisture content gradient is less than or equal to the lower limit of the ideal moisture content gradient range, the wood is determined to be over-dried. The second difference between the actual moisture content gradient and the lower limit of the ideal moisture content gradient range is calculated. If the second difference is greater than or equal to the second preset value, a second scrap instruction is generated. If the second difference is less than the second preset value, a second humidification treatment instruction is generated.

[0022] If the actual moisture content gradient is greater than or equal to the upper limit of the ideal moisture content gradient range, the wood is determined to be not completely dry. The third difference between the actual moisture content gradient and the upper limit of the ideal moisture content gradient range is calculated. If the third difference is greater than or equal to the third preset value, a second scrap instruction is generated. If the third difference is less than the third preset value, a second secondary drying treatment instruction is generated.

[0023] The second scrapping order, the second humidification treatment order, or the second secondary drying treatment order shall be defined as the secondary control order;

[0024] If the actual moisture content gradient is within the ideal moisture content gradient range, a timber qualification instruction will be generated.

[0025] Optionally, the processing terminals include a fine measurement area processing terminal and a coarse measurement area processing terminal; the initial control command and the fine measurement command are configured with corresponding first color development commands; the secondary control command and the timber qualification command are configured with corresponding second color development commands.

[0026] Optionally, the pre-stored timber profile database also includes the ideal equilibrium moisture content range corresponding to each timber species number, and

[0027] Receive the pre-process moisture content and post-process moisture content of each of the multiple key processes, and generate a moisture content change graph;

[0028] The system receives the factory equilibrium moisture content of timber after it has undergone multiple key processes; it compares the factory equilibrium moisture content with the ideal equilibrium moisture content range; if the factory equilibrium moisture content exceeds the ideal equilibrium moisture content range, it locates the abnormal time point based on the moisture content change graph; it obtains the current temperature and humidity of the production workshop corresponding to the abnormal time point; and it identifies the key process corresponding to the abnormal time point as the process to be tested.

[0029] Based on the abnormal time point, query the temperature and humidity of multiple historical production workshops, and count the proportion of the first data whose difference rate with the current production workshop temperature and humidity is less than the first preset similarity threshold among the multiple historical production workshop temperature and humidity; if the proportion of the first data does not reach the first preset ratio, generate a temperature and humidity equipment detection command and send it to the equipment maintenance terminal so that the corresponding maintenance personnel of the equipment maintenance terminal can check the abnormal equipment.

[0030] Based on the process to be tested, multiple historical process parameters are queried, and the proportion of second data among the multiple historical process parameters whose difference rate with the process parameters corresponding to the process to be tested is less than the second preset similarity threshold is calculated. If the proportion of second data does not reach the second preset ratio, a process step detection instruction is generated and sent to the process maintenance terminal so that the maintenance personnel corresponding to the process maintenance terminal can adjust the process parameters.

[0031] Optionally, the intelligent control method for production processes based on big data of the present invention further includes:

[0032] Timber with a factory equilibrium moisture content exceeding the ideal equilibrium moisture content range is identified as abnormal timber; the percentage of abnormal timber entering the warehouse within a preset period is calculated, and if the percentage of abnormal timber entering the warehouse exceeds the abnormal percentage threshold, the timber profile data of the corresponding timber type is updated.

[0033] Optionally, the intelligent control method for production processes based on big data of the present invention further includes:

[0034] Determine the sales location number based on the order number; determine the corresponding equilibrium moisture content at the sales location based on the sales location number, timber type number, and current time.

[0035] The factory equilibrium moisture content is compared with the sales location equilibrium moisture content. If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is less than a preset difference threshold, a shipping instruction is generated.

[0036] If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is greater than or equal to the preset difference threshold but less than the critical value, a pre-shipment calibration and control instruction will be generated.

[0037] Optionally, the intelligent control method for production processes based on big data of the present invention further includes:

[0038] If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is greater than or equal to the critical value, a prohibition on shipment order is generated, and temperature and humidity adjustment information for the production workshop at the sales location is generated; based on the timber type number, the remaining sales location numbers are matched; if the match is successful, a transfer shipment order is generated; if the match fails, an inventory order is generated.

[0039] Sending instructions, pre-shipment calibration and control instructions, transfer and shipment instructions, prohibition of shipment instructions, or inventory instructions to the cargo management terminal so that the corresponding management personnel at the cargo management terminal can control the shipment.

[0040] The present invention has the following beneficial effects:

[0041] 1. By constructing a closed-loop intelligent control process of "coarse screening, fine diagnosis, and gradient control," the accuracy and reliability of wood drying quality are fundamentally improved. Specifically, coarse screening quickly determines whether the wood moisture content is within the preset range, promptly identifying wood that is obviously over-dried or incompletely dried, and generating initial control instructions for preliminary correction. For wood that passes the coarse screening, fine screening is further performed to obtain accurate moisture content data, and the actual moisture content gradient is determined accordingly. By comparing it with the ideal moisture content gradient range, the uniformity of wood drying and the internal stress state can be comprehensively evaluated. When the gradient is abnormal, a secondary control instruction is automatically generated, requiring humidification or secondary drying. This replaces manual experience with a data-driven and intelligent approach, ensuring that the wood meets the moisture content standard while possessing a reasonable gradient distribution. As a result, the risk of cracking and deformation during subsequent processing and use of the wood is significantly reduced, improving the level of intelligence in the production process and the stability of the finished products.

[0042] 2. Improved the intelligence level, process controllability, and product quality stability of timber production. Specifically, the system first receives and analyzes moisture content change graphs to quickly locate abnormal process steps. By comparing the real-time environment and process parameters of this step with massive historical data, the system can intelligently determine whether the root cause of the problem stems from a malfunction in temperature and humidity equipment or inaccurate process parameter settings, and generates targeted maintenance instructions (temperature and humidity equipment detection instructions or process step detection instructions). This guides maintenance personnel to conduct precise troubleshooting, greatly improving the efficiency of anomaly handling and avoiding resource waste caused by blind adjustments. Furthermore, through dynamic statistics and trend analysis of the proportion of abnormal timber entering the warehouse, the system can adaptively update timber profile data, ensuring that process control standards remain dynamically consistent with actual production. This effectively addresses differences in raw material characteristics and changes in new market demands, fundamentally preventing the recurrence of similar quality problems. Thus, the production quality control system has achieved a leap from "static execution" to "dynamic optimization," significantly improving the intelligence level, process controllability, and product quality stability of timber production.

[0043] 3. Improved product quality and delivery reliability. Specifically, the system predicts the equilibrium moisture content of timber at the sales location based on order information and uses this as the final quality target for the timber. By comparing the factory equilibrium moisture content with this target, precise control and risk management can be achieved: for timber with moderate differences, a pre-shipment calibration and control instruction is generated, including an adjustment period and a target adjustment moisture content value or range calculated based on the heating season environment, ensuring that the timber is fine-tuned to the most suitable state for the end-user environment before leaving the factory; for timber with excessive differences, a prohibition on shipment instruction is generated, and at the same time, intelligent matching and transfer shipment instructions are generated for other sales locations, or an inventory instruction is generated, separating the risky timber from the original order. Through this three-level linkage control (direct shipment / calibration / prohibition of shipment) based on end-user environment prediction, timber quality control standards are extended from internal factory standards to end-user standards, fundamentally preventing quality defects such as cracking and deformation caused by environmental differences, and significantly improving product delivery reliability, market adaptability, and customer satisfaction. Attached Figure Description

[0044] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0045] Figure 1 This is a flowchart of a production process intelligent control method based on big data according to the present invention;

[0046] Figure 2 This is a schematic diagram of the heartwood and sapwood of a wood cross-section, representing a production process intelligent control method based on big data according to the present invention.

[0047] Figure 3 This is a flowchart of the sales location adaptation and delivery decision-making process of a production process intelligent control method based on big data according to the present invention. Detailed Implementation

[0048] The invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the drawings and embodiments of the invention are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0049] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0050] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0051] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0052] The names of messages or information exchanged between the various devices of this invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0053] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0054] like Figure 1 The diagram shows a flowchart of an intelligent control method for production processes based on big data according to the present invention, which specifically includes the following steps:

[0055] Step 101: Obtain timber information, including timber species number and order number; Based on the timber species number and the pre-stored timber profile database, determine the preset probe coarse measurement point location information. The pre-stored timber profile database includes multiple timber species numbers, a preset moisture content range corresponding to each timber species number, an ideal moisture content gradient range, and the preset probe coarse measurement point location information.

[0056] Step 102: Send the probe coarse measurement point location information to the coarse measurement terminal so that the testing personnel corresponding to the coarse measurement terminal can measure the moisture content of the wood and obtain the coarse moisture content.

[0057] In some embodiments, the execution entity of the intelligent control method for production processes based on big data according to the present invention is a backend server. RFID (Radio Frequency Identification) tags are pre-attached to the timber, and the RFID tags contain a timber species number to identify the timber type and an order number to correspond to the sales order. The backend server receives and parses the timber species number and order number through communication with an RFID reader. In the event of missing or unavailable RFID, the backend server can also obtain timber information through barcode scanning, QR code recognition, or manual entry. The timber information is basic data used to identify and track the timber's identity and origin. In this step, the timber species number and order number are specifically referred to. The timber species number is a unique code identifying the timber species, determining the timber's physical characteristics and testing accuracy requirements. The order number is associated with a specific sales order and is used to track the order's execution status and final destination. After obtaining the timber species number, the backend server accesses a pre-stored timber profile database based on that timber species number to query the timber profile data corresponding to the timber species number. The timber profile database is a structured data table pre-stored by the backend server, which can be constructed using a relational database or a key-value database. Each timber profile includes at least: the timber species number, the corresponding preset moisture content range, the ideal moisture content gradient range, and the preset probe coarse measurement point location information. The preset moisture content range is the allowable range of timber moisture content during the coarse measurement stage. The ideal moisture content gradient range is a reasonable range (e.g., 3% to 6%) for the difference in moisture content between heartwood and sapwood, used to assess drying uniformity. The backend server determines the absolute probe point locations for subsequent testing based on the probe coarse measurement point location information matched by the timber species number, combined with the actual geometric dimensions of the timber. Specifically, the probe coarse measurement point location information is stored in the database as a template, which includes several relative coordinate points and their corresponding sampling roles (e.g., heartwood or sapwood). After receiving the actual width, thickness, and length data of the timber, the backend server scales and maps the relative coordinates in the template to obtain the absolute probe measurement point locations on the timber piece. For example, for mahogany timber, additional measurement points can be set in the middle of the timber length in the template to meet the needs of fine testing of high-value timber.

[0058] In some embodiments, the backend server packages the determined probe coarse measurement point location information into a data message and sends it to the coarse measurement terminal via a network interface. The data message may include the wood's RFID tag, order number, absolute coordinates of each measurement point on the cross-section, suggested probe insertion depth, and preset moisture content range. Upon receiving this information, the coarse measurement terminal can generate a corresponding cross-sectional diagram on its display interface and visually mark the probe measurement points to prompt the testing personnel to perform moisture content testing at the corresponding locations. The coarse measurement terminal is a handheld tablet device whose display interface can visually display the cross-sectional measurement point information sent by the backend server and graphically mark each probe point on the interface, thus prompting the testing personnel to operate according to the point. After receiving the instruction, the testing personnel only need to follow the on-screen prompts to insert the probe of the moisture content testing instrument into the corresponding position on the wood and read the value. The moisture content testing instrument can be a resistance-type handheld moisture content tester, which calculates the moisture content of the wood by detecting the resistance value between the probes. After measurement, personnel can input the measured moisture content value through the touch interface of the coarse testing terminal, or the testing instrument can automatically upload it to the coarse testing terminal via wireless connection. The coarse testing terminal then sends the measurement results to the backend server in real time. Upon receiving the coarse moisture content data, the backend server stores it and performs subsequent comparative analysis. Coarse moisture content refers to the moisture content value of wood obtained through a quick and simple method during the wood production process. Its accuracy is usually lower than that of precise measurement, but it is sufficient for a preliminary assessment of the wood's moisture content.

[0059] Step 103: Receive the rough moisture content and compare it with the preset moisture content range to obtain the first comparison result. The first comparison result is used to characterize the initial adjustment or retesting of the moisture content of the wood.

[0060] Step 104: If the first comparison result indicates that the moisture content of the wood needs to be retested, then a fine measurement instruction is sent to the corresponding processing terminal so that the testing personnel at the processing terminal can measure the moisture content of the wood and obtain the fine measurement moisture content.

[0061] In some embodiments, the backend server receives data packets containing the coarse moisture content uploaded from the coarse testing terminal in real time via a network interface. These data packets contain moisture content measurements from multiple probe points bound to the wood's RFID tag. The backend server parses the data packet and extracts key characteristic values, such as calculating the arithmetic mean of all measurement points or selecting the heartwood measurement point as the representative moisture content for this coarse test. Subsequently, the backend server uses the wood species number of the current wood as an index to query the corresponding preset moisture content range from a pre-stored wood profile database. The extracted coarse moisture content is compared with the preset moisture content range, and a first comparison result is generated according to preset logical rules. The first comparison result is the judgment result obtained by the backend server after comparing the coarse moisture content with the preset moisture content range. The preset moisture content range is the ideal moisture content range for the wood stored in the wood profile. The first comparison result represents the initial adjustment or retesting of the wood's moisture content. Initial adjustment is a preliminary correction treatment for wood whose coarse moisture content significantly exceeds the preset range, including humidification treatment (over-drying) or secondary drying treatment (incomplete drying). Retested moisture content refers to moisture content that needs to be measured further using more precise methods (such as multi-needle sensors or infrared methods).

[0062] In some embodiments, after obtaining the first comparison result, the logic judgment module of the backend server determines whether the result is "perform a retest of moisture content". If the determination is yes, the instruction scheduling module of the backend server immediately generates a precision measurement instruction. This instruction is a structured control command, the content of which includes at least the RFID tag of the target wood, the instruction type (precision measurement), and necessary contextual information (such as the wood species number). The backend server accurately sends the precision measurement instruction to the processing terminal corresponding to the precision measurement station (specifically, the precision measurement area processing terminal in this scenario) through its network interface. After receiving the instruction, the precision measurement area processing terminal notifies the inspection personnel at the station through its graphical interface (e.g., pop-up window, highlighting, or sound prompt) and displays the identification information of the wood to be tested. The inspection personnel locate the corresponding wood according to the instruction guidance and perform the precision measurement operation using a more precise moisture content measuring device. The precision measurement device can be a multi-needle moisture content sensor, which can simultaneously penetrate the heartwood and sapwood areas of the wood cross-section (e.g., ...). Figure 2The diagram illustrates a schematic of the heartwood and sapwood cross-section of wood using a big data-based intelligent control method for production processes according to the present invention. This method acquires precise moisture content data from multiple points simultaneously. Alternatively, an infrared imaging system can be used to acquire the moisture content distribution map of the wood cross-section through non-contact scanning. After the testing personnel complete the measurement, the measuring device can automatically upload the precisely measured moisture content data (such as specific values ​​at multiple points or calculated moisture content gradient values) to the precision measurement area processing terminal via wired or wireless means, or the testing personnel can manually input the data based on the device readings. The precision measurement area processing terminal then performs preliminary data encapsulation and prepares to send it back to the backend server for subsequent calculation of the actual moisture content gradient and depth assessment. Precise moisture content refers to the moisture content data of the heartwood and sapwood of wood measured using high-precision methods such as needle penetration (e.g., multi-needle moisture sensor) or infrared methods (e.g., infrared imaging system). In the field of wood processing, moisture content refers to the ratio of the mass of water contained in the wood to the oven-dry mass of the wood (i.e., the mass of the wood when completely dry and free of any moisture), usually expressed as a percentage.

[0063] Step 105: Receive the measured moisture content and determine the actual moisture content gradient; compare the actual moisture content gradient with the ideal moisture content gradient range to obtain a second comparison result, which is used to characterize whether the wood has undergone secondary regulation or is qualified.

[0064] In some embodiments, the background server receives the data of the moisture content measured precisely from the processing terminal in the precise measurement area through a network interface. This data contains detailed moisture content information that can distinguish between heartwood and sapwood, measured by a multi-pin moisture sensor or an infrared imaging system. The background server calculates the actual moisture content gradient based on these precisely measured moisture content data. The specific calculation method is as follows: calculate the average moisture content of each measurement point in the heartwood area as the representative value of the heartwood, calculate the average moisture content of each measurement point in the sapwood area as the representative value of the sapwood, and then subtract the representative value of the heartwood from the representative value of the sapwood. The obtained difference is the actual moisture content gradient. For example, when the average moisture content of the heartwood is measured to be 18% and the average moisture content of the sapwood is 22%, the background server calculates the actual moisture content gradient to be 4%. Subsequently, the background server queries the corresponding ideal moisture content gradient interval from the pre-stored wood portrait database with the wood species number of the current wood as the index. This module compares the calculated actual moisture content gradient with the ideal moisture content gradient interval to generate a second comparison result. The actual moisture content gradient refers to the moisture content difference value calculated by the background server based on the precisely measured moisture content of the heartwood and sapwood. This gradient (the difference between the moisture content of the heartwood and the moisture content of the sapwood) is the core index for measuring the drying uniformity and internal stress of the wood. The second comparison result is the judgment conclusion obtained by the background server after comparing the actual moisture content gradient with the ideal moisture content gradient interval. This conclusion directly determines the final direction of the wood: whether to perform secondary regulation or determine that the wood is qualified. Secondary regulation refers to the second and more refined corrective treatment that needs to be taken when the wood gradient is abnormal (too small or too large), such as buffer aging, secondary drying, or scrapping / downgrading. Wood being qualified is a status determination issued by the background server, specifically referring to that after passing the precise moisture content measurement link, the actual moisture content gradient of the wood meets the requirements of the ideal moisture content gradient interval.

[0065] Step 106, generate a primary regulation instruction, a precise measurement instruction, a secondary regulation instruction, or a wood qualification instruction according to the first comparison result or the second comparison result and send them to the corresponding processing terminal.

[0066] In some embodiments, after the judgment is completed based on the coarse moisture content, the backend server executes the following instruction generation logic: If the coarse moisture content is less than or equal to the lower limit (threshold A) of the preset moisture content range, the backend server determines that the wood is over-dried. Subsequently, a first difference between the coarse moisture content and the lower threshold is calculated. If the first difference is greater than or equal to a first preset value (e.g., 8%), a first scrap instruction is generated; if the first difference is less than the first preset value, a first humidification treatment instruction is generated. If the coarse moisture content is greater than or equal to the upper limit (threshold B) of the preset moisture content range, the backend server determines that the wood is not completely dry and directly generates a first and second drying treatment instruction. The backend server uniformly determines the first scrap instruction, the first humidification treatment instruction, or the first and second drying treatment instruction as an initial control instruction and sends it to the coarse moisture content area processing terminal or the corresponding execution device (such as a spray humidification device or drying equipment). If the coarse moisture content is within the preset moisture content range, the backend server generates a fine moisture content instruction and sends it to the fine moisture content area processing terminal to start the fine moisture content process. The first difference refers to the absolute difference between the roughly measured moisture content and the lower limit (threshold A) of the preset moisture content range. The first preset value is a critical threshold set by the system to determine whether the over-drying of the wood is reversible; it is a fixed percentage value (e.g., 8%). The first scrap instruction is a control instruction generated by the backend server when it determines that the degree of over-drying of the wood exceeds the repairable range. This instruction indicates that the wood is judged as unqualified by the system and cannot enter any subsequent production stage. Its content usually includes the RFID number of the target wood and the instruction type (scrap). The first humidification treatment instruction is a control instruction generated by the backend server when it determines that although the wood is over-drying, the degree is within the repairable range. This instruction requires the wood to be humidified to increase its overall moisture content. Its content usually includes the RFID number of the target wood, the instruction type (humidification), and sometimes humidification process parameters. The first and second drying treatment instructions are a control instruction generated by the backend server when it determines that the wood is not completely dry. This instruction is triggered and requires the wood to continue drying. Its content typically includes the RFID number of the target timber and the instruction type (secondary drying). The initial control instruction is a set of instructions generated by the backend server based on the preliminary assessment results of the coarse moisture content (i.e., the first comparison result), used to perform the first corrective treatment on timber with severely abnormal moisture content. It includes the first scrap instruction, the first humidification treatment instruction, or the first secondary drying treatment instruction. The fine measurement instruction is a control instruction generated by the backend server. When it determines that the coarse moisture content of the timber is within a qualified preset moisture content range, this instruction is triggered. This instruction signifies that the timber has passed the initial screening and is eligible to enter the more refined testing stage. Its content typically includes the RFID number of the target timber and the instruction type (fine measurement). The coarse measurement area processing terminal is an intelligent terminal device (such as an industrial tablet PC) deployed at the timber coarse measurement station.

[0067] In some embodiments, after the judgment is completed based on the precise moisture content measurement, the backend server executes the following instruction generation logic: If the actual moisture content gradient is less than or equal to the lower limit of the ideal moisture content gradient range, the backend server determines that the wood moisture content distribution is uneven and the gradient is too small. Subsequently, a second difference between the actual moisture content gradient and the lower limit of the ideal gradient range is calculated. If the second difference is greater than or equal to a second preset value, a second scrap instruction is generated; if the second difference is less than the second preset value, a second humidification treatment instruction is generated. If the actual moisture content gradient is greater than or equal to the upper limit of the ideal moisture content gradient range, the backend server determines that the wood moisture content gradient is too large. Subsequently, a third difference between the actual moisture content gradient and the upper limit of the ideal gradient range is calculated. If the third difference is greater than or equal to a third preset value, a second scrap instruction is generated; if the third difference is less than the third preset value, a second secondary drying treatment instruction is generated. The backend server uniformly determines the second scrap instruction, the second humidification treatment instruction, or the second secondary drying treatment instruction as a secondary control instruction and sends it to the corresponding processing terminal or control device. If the actual moisture content gradient is within the ideal moisture content gradient range, the backend server generates a timber qualification instruction, allowing the timber to proceed to subsequent processing stages. The second difference is the absolute difference between the actual gradient value and the lower limit of the ideal moisture content gradient range when the actual moisture content gradient is less than or equal to the lower limit. The second preset value is a preset critical threshold used to determine whether the "gradient too small" problem is reversible. The second scrap instruction is generated by the backend server during the precision testing phase. It is triggered when the "gradient too small" or "gradient too large" problem becomes irreparable. This instruction indicates that the timber is judged as unqualified due to extremely poor drying uniformity, and the timber status is updated to "scrap" based on this instruction. The second humidification treatment instruction is generated by the backend server during the precision testing phase. It is triggered when the timber is determined to have a "gradient too small" but the problem is repairable. This instruction requires gentle humidification or buffer aging of the timber to allow the sapwood to absorb appropriate moisture and restore a reasonable moisture content gradient. The third difference is the absolute difference between the actual gradient value and the upper limit of the ideal moisture content gradient range when the actual moisture content gradient is greater than or equal to the upper limit of the range. The third preset value is a set critical threshold used to determine whether the "excessive gradient" problem is reversible. The second drying treatment instruction is an instruction generated by the backend server during the precision measurement stage. It is triggered when it is determined that the wood has an "excessive gradient" but the problem is repairable. This instruction requires the wood to continue drying or aging treatment, with the aim of allowing the heartwood moisture to slowly diffuse outward, eventually bringing the moisture content gradient back to the ideal range. The secondary control instruction is a collective term formed by combining the second scrap instruction, the second humidification treatment instruction, and the second drying treatment instruction.The timber qualification instruction is the final release instruction generated by the back-end server during the precision testing stage. It is triggered when the server determines that the actual moisture content gradient of the timber is within the ideal moisture content gradient range. This instruction is the "highest certification" of the timber in terms of drying quality, indicating that its drying uniformity fully meets the standards, its internal stress is small, and it can safely flow into the subsequent processing stage or leave the factory.

[0068] The processing terminals include a fine measurement area processing terminal and a coarse measurement area processing terminal; the initial control command and the fine measurement command are configured with corresponding first color development commands; the secondary control command and the timber qualification command are configured with corresponding second color development commands.

[0069] In some embodiments, the coarse moisture content processing terminal refers to an intelligent terminal device deployed at the coarse moisture content testing station of timber, typically an industrial-grade tablet or a rugged handheld device. This terminal primarily performs the following functions: receiving and displaying the probe coarse moisture content point location information issued by the backend server, guiding the testing personnel through a graphical interface; receiving and displaying decision instructions based on the coarse moisture content results, i.e., initial adjustment instructions or fine measurement instructions; and providing a data entry interface for testing personnel to upload the measured coarse moisture content to the backend server. The fine moisture content processing terminal refers to an intelligent terminal device deployed at the fine moisture content testing station of timber, which can be an industrial PC or a high-performance tablet. This terminal primarily performs the following functions: receiving and displaying fine measurement instructions issued by the backend server, notifying testing personnel to perform fine measurement on specific timber; receiving and displaying decision instructions based on the fine measurement results, i.e., secondary adjustment instructions or timber qualification instructions; and receiving fine moisture content data from precision measuring equipment (such as multi-needle sensors or infrared imaging systems), or providing an interface for testing personnel to manually enter the data and uploading it to the backend server. The first color-changing command is tied to the decision result of the coarse testing area and is used to control the first multi-color light installed in the coarse testing area. Its configuration rules are as follows: When the backend server generates the initial control command, the first color-changing command will control the first multi-color light to display a specific color according to the specific content of the command. The first humidification treatment command corresponds to a blue light. The first secondary drying treatment command corresponds to a yellow light. The first scrap command corresponds to a red light. When the backend server generates a fine testing command, the first color-changing command will control the first multi-color light to display a green light, indicating that the timber can be transferred to the next stage (fine testing area). The second color-changing command is tied to the decision result of the fine testing area and is used to control the second multi-color flashing light installed in the fine testing area. Its configuration rules are as follows: When the backend server generates a secondary control command, the second color-changing command will control the second multi-color flashing light to display a specific color according to the command content, and may activate a flashing mode to attract special attention. The second humidification treatment command corresponds to a blue light (may flash). The second secondary drying treatment command corresponds to a yellow light (may flash). The second scrap command corresponds to a red light (usually flashing). When the backend server generates a timber qualification instruction, the second color-coding instruction controls the second multi-color flashing light to display a stable green light. In practice, for high-value or high-requirement timber species such as mahogany, the backend server will additionally trigger a light flashing mode when issuing the second color-coding instruction. For example, when mahogany requires secondary drying, the yellow light flashes; when it is deemed qualified, the green light may flash rapidly a few times before turning solid to indicate its special status and operational importance. Operators in the coarse testing area mainly focus on the screen prompts on the coarse testing area processing terminal and the color of the first multi-color light to quickly decide whether to send the timber to the humidification room, drying kiln, scrap area, or fine testing area. Operators in the fine testing area mainly focus on the screen prompts on the fine testing area processing terminal and the color and status of the second multi-color flashing light to quickly decide whether to send the timber back for secondary adjustment, release it to the next process, or move it to the scrap / return area.

[0070] In these embodiments, a closed-loop intelligent control process of "coarse screening, fine diagnosis, and gradient control" is constructed, fundamentally improving the accuracy and reliability of wood drying quality. Specifically, coarse screening first quickly determines whether the wood moisture content is within a preset range, promptly identifying wood that is obviously over-dried or incompletely dried, and generating initial control instructions for preliminary correction. For wood that passes the coarse screening, fine screening is further performed to obtain accurate moisture content data, and the actual moisture content gradient is determined accordingly. By comparing it with the ideal moisture content gradient range, the uniformity of wood drying and the internal stress state can be comprehensively evaluated. When the gradient is abnormal, a secondary control instruction is automatically generated, requiring humidification or secondary drying. This replaces manual experience with a data-driven and intelligent approach, ensuring that the wood meets the moisture content standard while possessing a reasonable gradient distribution. As a result, the risk of cracking and deformation during subsequent wood processing and use is significantly reduced, improving the intelligence level of the production process and the stability of the finished products.

[0071] In some embodiments, to further address the second technical problem described in the background section, namely, "the existing technology lacks the ability to correlate and self-optimize the quality data throughout the entire wood production process, resulting in the inability to accurately trace the root cause of quality problems leaving the factory, and the inability to dynamically adjust production process standards based on actual production big data, leading to the continuous occurrence of similar quality problems and low production efficiency," in some embodiments of the present invention, the pre-stored wood profile database also includes the ideal equilibrium moisture content range corresponding to each wood species number, and

[0072] Step 1: Receive the pre-process moisture content and post-process moisture content of each of the multiple key processes, and generate a moisture content change graph.

[0073] In some embodiments, the data content contained in the pre-stored timber profile database is further expanded based on the previously defined preset moisture content range, ideal moisture content gradient range, and probe coarse measurement point location information. A key parameter, the ideal equilibrium moisture content range, is added for each timber species number. The ideal equilibrium moisture content range is the ideal target range for the final moisture content of a specific timber species after all production and processing is completed and it is ready for shipment. This range considers the need for timber to maintain dimensional stability under normal factory storage conditions (e.g., standard temperature and humidity conditions in a factory warehouse). The difference between the preset moisture content range and the ideal equilibrium moisture content range is that the preset moisture content range is used during the production process (coarse measurement stage) to determine whether the timber is dry enough for subsequent processing; while the ideal equilibrium moisture content range is used at the end of the production process (before shipment) to determine whether the final moisture content quality of the finished timber meets the standards. This range value is associated with the timber species number and stored in the timber profile database in the same way as described above. Key processes refer to the stages in the timber production process that significantly affect moisture content. These processes include, but are not limited to, cutting / planing, splicing / pressing, etc. At each critical process equipment station (such as cutting machines and pressing machines), RFID readers or moisture content measuring devices connected to the processing terminal are deployed. When timber with RFID tags enters a critical process station, the operator or automated equipment immediately measures the moisture content before processing begins, as the pre-process moisture content. After the processing is completed, the moisture content is measured again immediately, as the post-process moisture content. The processing terminal (or the data collector at the station) packages this pair of pre-process and post-process moisture content data, along with the corresponding process number, timestamp, and timber RFID tag, and uploads it to the backend server. The pre-process moisture content refers to the current moisture content obtained by the backend server by reading the RFID tag or receiving data uploaded by the detection terminal before the timber enters a critical process stage. The post-process moisture content refers to the latest moisture content obtained by the backend server in the same way after the timber completes a critical process stage. The backend server stores this time-series data in close association with the specific individual timber (via RFID tag) and the process stage (via process number).

[0074] Based on this, when a piece of timber completes all production processes and is ready to leave the factory, the back-end server uses the timber's RFID tag as an index to retrieve all moisture content data recorded at each key process point (including before and after processing), as well as the final equilibrium moisture content at the factory, from the database. Using these data points, the back-end server automatically generates a moisture content change graph. In this graph: the horizontal axis represents time, clearly showing the flow of timber throughout the entire production cycle. The vertical axis represents moisture content. The graph marks the moisture content values ​​at the beginning and end of each key process, forming a dynamic change curve. This graph visually reproduces the "moisture trajectory" of a single piece of timber throughout the entire processing. It is not only an important quality traceability record, but more importantly, it provides an intuitive and accurate data foundation for the back-end server to locate abnormal process steps in subsequent steps. For example, if the moisture content at the factory is unqualified, by observing this graph, it is possible to quickly pinpoint after which the abnormal moisture content fluctuation began.

[0075] Step 2: Receive the factory equilibrium moisture content of the wood after it has undergone multiple key processes; compare the factory equilibrium moisture content with the ideal equilibrium moisture content range; if the factory equilibrium moisture content exceeds the ideal equilibrium moisture content range, locate the abnormal time point according to the moisture content change graph; obtain the current temperature and humidity of the production workshop corresponding to the abnormal time point; and designate the key process corresponding to the abnormal time point as the process to be tested.

[0076] In some embodiments, before the timber completes all production and processing and is ready for warehousing or shipment, the final inspection station performs a final moisture content measurement on the timber using a processing terminal or dedicated measuring equipment to obtain the factory equilibrium moisture content. This data is uploaded to the backend server in real time. Upon receiving this data, the backend server immediately queries the timber profile database using the timber species number as the key to obtain the corresponding ideal equilibrium moisture content range (threshold C and threshold D). Subsequently, the factory equilibrium moisture content is compared with this range. The comparison result has only two possibilities: qualified or unqualified. If the factory equilibrium moisture content is within the range, it is determined to be qualified, and the process ends. Only when the comparison result shows that the factory equilibrium moisture content exceeds (i.e., less than threshold C or greater than threshold D) the ideal equilibrium moisture content range will the backend server automatically trigger the subsequent anomaly diagnosis and traceability process. Specifically, if the factory equilibrium moisture content is lower than the lower limit of the ideal range (threshold C), the backend server will look for the time point in the graph where the last moisture content measurement value is lower than or equal to threshold C. If the factory equilibrium moisture content is higher than the upper limit of the ideal range (threshold D), the backend server will search for the time point in the graph where the last moisture content measurement was higher than or equal to threshold D. The time point determined by this logic is the abnormal time point. This time point marks the critical moment when the moisture content of the wood deviated from the final target and failed to be corrected, indicating the beginning of the problem. After locating the abnormal time point, the backend server will query the environmental monitoring database for the data recorded by the temperature and humidity sensors in the production workshop where the wood is located at that exact time point. This data is called the current temperature and humidity of the production workshop, reflecting the real-time environmental conditions of that workstation when the problem occurred. Based on the abnormal time point, the backend server will perform a reverse query in the production log of that wood to determine which critical process was being executed or had just completed at that time point or the closest time period. This located critical process is marked as the process to be tested. For example, if the abnormal time point located by the system coincides with the end time of the "cutting" process, then the "cutting" process is identified as the process to be tested for the abnormal factory moisture content. The backend server performs a reverse query in the timber production log based on the abnormal time point to determine which critical process was being executed or had just completed within that time point or the closest time period. This identified critical process is then marked as the process to be inspected. For example, if the identified abnormal time point coincides with the end time of the "cutting" process, then the "cutting" process is identified as the process to be inspected for abnormal moisture content at the time of shipment. The equilibrium moisture content at the time of shipment refers to the final moisture content measured after all critical processes have been completed and before the timber is ready to leave the warehouse. This data is the ultimate basis for assessing whether the dried timber quality meets the factory standards.An abnormal time point refers to a point in time on the moisture content change graph where the wood moisture content undergoes an unexpected or drastic change. This time point is often directly related to key processes or environmental factors that lead to unqualified equilibrium moisture content at the factory gate. Current production workshop temperature and humidity refer to real-time environmental data (such as temperature and relative humidity) obtained by the backend server from the workshop environmental monitoring system at the time the abnormal time point occurs. The process to be tested refers to a key process step identified by the backend server through analysis of the moisture content change graph, where its execution is suspected to have problems.

[0077] Step 3: Based on the abnormal time point, query the temperature and humidity of multiple historical production workshops, and count the proportion of the first data whose difference rate with the current production workshop temperature and humidity is less than the first preset similarity threshold. If the proportion of the first data does not reach the first preset ratio, generate a temperature and humidity equipment detection command and send it to the equipment maintenance terminal so that the maintenance personnel corresponding to the equipment maintenance terminal can check the abnormal equipment.

[0078] In some embodiments, after the backend server locates an abnormal time point and the corresponding current temperature and humidity in the production workshop, it will perform a historical data query. The query conditions are: the same production workshop, the same process step, and a specific time point that is the same as or close to the abnormal time point (e.g., the same time every day within the past 30 days). The backend server retrieves a set of historical production workshop temperature and humidity data that meet the conditions from the environmental monitoring historical database. The backend server compares the current production workshop temperature and humidity (the data from the abnormal point) with the set of historical production workshop temperature and humidity data obtained from the query one by one. For each historical data point, the difference rate between it and the current data is calculated. The difference rate is equal to the absolute value of the difference between the current data value and the historical data value, divided by the historical data value, and then multiplied by 100%. The backend server presets a similarity threshold (e.g., 5%). If the difference rate between a historical data point and the current data is less than this threshold, the historical data point is considered "similar" to the current environment. The backend server counts all "similar" historical data points and calculates their percentage of the total historical data, thus obtaining the first data proportion. Subsequently, the backend server compares the calculated first data percentage with another preset value, namely the first preset ratio (e.g., 10%). If the first data percentage is less than the first preset ratio (e.g., only 5%), it means that the environmental data at the current anomaly point is significantly different from historical normal conditions. This strongly suggests that it is not a normal fluctuation in the workshop environment, but rather that the temperature and humidity sensor itself may be malfunctioning or inaccurate. Based on this judgment, the backend server immediately generates a temperature and humidity equipment detection command. This command includes the location information of the abnormal sensor, the abnormal time point, relevant data, and the calculation result of the first data percentage. The backend server sends the generated temperature and humidity equipment detection command to the equipment maintenance terminal through the network interface. After receiving the command, the maintenance personnel at the equipment maintenance terminal can locate the specific temperature and humidity sensor based on the information in the command, and perform on-site inspection, calibration, or repair, thereby eliminating production quality problems caused by environmental monitoring equipment failure. Among them, multiple historical production workshop temperature and humidity data refer to a large amount of historical environmental data (including temperature and humidity) stored in the historical database by the backend server and associated with the abnormal time point and the process to be tested. The current temperature and humidity in the production workshop refer to the real-time temperature and humidity data obtained by the backend server at an abnormal time point, i.e., the environmental parameters during the processing of the substandard wood. The first preset similarity threshold is a pre-set percentage or numerical range used to define the degree of similarity between the current temperature and humidity and historical temperature and humidity. If the difference rate is less than this threshold, the environmental conditions are considered similar. The first data proportion refers to the proportion of historical temperature and humidity data that is similar to the current temperature and humidity (i.e., the difference rate is less than the first preset similarity threshold) out of the total historical data. The first preset ratio is a pre-set percentage threshold used to determine whether the current temperature and humidity have historical reference value.If the percentage of the first data point does not reach this ratio, the current temperature and humidity environment is considered to be either extremely rare or exceptionally abnormal. Temperature and humidity equipment detection commands are instructions issued by the backend server to the operations and maintenance department, requiring the inspection and maintenance of environmental monitoring or control equipment (such as temperature and humidity sensors, air conditioners, dehumidifiers, etc.) in the production workshop. Equipment maintenance terminals are devices that receive temperature and humidity equipment detection commands and are typically used by operations and maintenance personnel.

[0079] Step 4: Based on the process to be tested, query multiple historical process parameters, and count the proportion of second data among the multiple historical process parameters whose difference rate with the process parameters corresponding to the process to be tested is less than the second preset similarity threshold. If the proportion of second data does not reach the second preset ratio, generate a process step detection instruction and send it to the process maintenance terminal so that the maintenance personnel corresponding to the process maintenance terminal can adjust the process parameters.

[0080] In some embodiments, the backend server performs a query in the historical process parameter database, indexed by the process to be detected (e.g., "cutting"). The query criteria are the same process number and a specific time point that is the same as or close to the abnormal time point (e.g., a time point within the past 30 days when the same process was executed). The server retrieves a set of historical process parameter groups that meet the criteria, each containing historical values ​​for multiple parameters such as "cutting speed," "cutting depth," "pressing pressure," and "pressing time" for that process. This dataset represents the typical parameter settings when the process was executed normally in the past. Subsequently, the backend server performs a detailed, parameter-by-parameter comparison between the current process parameter group corresponding to the abnormal time point of the process to be detected and each of the retrieved historical process parameter groups. For each process parameter (e.g., "cutting speed"), the difference rate between the current setting and each historical setting is calculated. The difference rate is equal to the absolute value of the difference between the current parameter value and the historical parameter value, divided by the historical parameter value, and then multiplied by 100%. The backend server uses or presets a similarity threshold (e.g., 5%) for process parameter comparison. If the difference between a historical parameter value and the current parameter value is less than a certain threshold, the historical parameter value is considered "similar" to the current setting. The backend server then performs statistics, calculating the percentage of historical data entries that are "similar" to each process parameter out of the total number of historical data entries; this is the second data percentage for that parameter. For example, the second data percentage for the "cutting speed" parameter is 15%, and the second data percentage for the "cutting depth" parameter is 60%. Next, the backend server compares the second data percentage for each parameter with a preset second preset ratio (e.g., 10%). If the second data percentage for a parameter does not reach (i.e., is less than) the second preset ratio, the backend server determines that the process parameter is a rare, abnormal process parameter that deviates from the historical normal range under the current settings. Based on this determination, the backend server generates a process step detection instruction. This instruction clearly indicates the process number to be detected and lists all identified abnormal process parameters and their detailed information. This instruction is sent to the process maintenance terminal (such as the process engineer's computer) via a network interface. Upon receiving the instruction, the maintenance personnel (usually process engineers or equipment commissioning personnel) at the process maintenance terminal can repair the specified process equipment or check and adjust abnormal process parameters according to the instruction information, thereby ensuring the stability and accuracy of process execution and eliminating product quality problems caused by process deviations at their root. The second preset similarity threshold is a pre-set threshold used to define the degree of similarity between the current process parameters and historical process parameters. If the difference rate is less than this threshold, the historical process settings are considered similar to the current ones. Multiple historical process parameters constitute a dataset, referring to a large number of historical parameter records associated with the process under test, retrieved by the backend server from the historical database.Process parameters refer to the specific numerical variables that need to be set when controlling production equipment to execute a particular key process. The second data percentage is a statistically derived percentage, representing the proportion of data entries in multiple historical process parameter datasets that are "similar" to the current process parameter to be detected (i.e., the difference rate is less than the second preset similarity threshold), out of the total number of historical data entries. The second preset ratio is a pre-set critical percentage threshold (e.g., 10%) used to make a final diagnostic decision on the process parameters. The process step detection command is a control command generated by the backend server, triggered when it determines that the second data percentage of one or more process parameters has not reached the second preset ratio.

[0081] The present invention provides an intelligent control method for production processes based on big data, which further includes:

[0082] Timber with a factory equilibrium moisture content exceeding the ideal equilibrium moisture content range is identified as abnormal timber; the percentage of abnormal timber entering the warehouse within a preset period is calculated, and if the percentage of abnormal timber entering the warehouse exceeds the abnormal percentage threshold, the timber profile data of the corresponding timber type is updated.

[0083] In some embodiments, the backend server marks all timber whose factory equilibrium moisture content is determined to be outside the ideal equilibrium moisture content range during the final inspection as abnormal timber, and records the corresponding timber species number and specific abnormal moisture content value. Based on this, the backend server sets a preset period (e.g., two weeks). Within this period, the following calculations are performed: the number of timber marked as abnormal and belonging to the same timber species number (e.g., timber Q) is counted as the numerator. The total number of timber species numbered (timber Q) entering the warehouse within the same preset period is counted as the denominator. The entry percentage is then calculated by dividing the number of abnormal timber of the same timber species by the total number of timber species entering the warehouse, and then multiplying by 100%. Based on this, the backend server compares the calculated entry percentage with a preset abnormal percentage threshold (e.g., 60%). If the entry percentage exceeds the abnormal percentage threshold (e.g., within a 14-day monitoring period, the abnormal entry percentage of timber Q is higher than 60% for 11 consecutive days), this constitutes strong statistical evidence. This indicates that the currently recorded "anomalies" are not accidental production deviations, but rather likely a new normal. This could be due to a systematic change in the initial characteristics of the timber raw materials, new requirements from the customer's usage environment, or the existing ideal equilibrium moisture content range no longer being applicable. Based on these judgments, the backend server will perform a crucial optimization operation: updating the timber profile data for the corresponding timber species. Specifically, the backend server will correct the ideal equilibrium moisture content range corresponding to the timber species number (timber Q) in the timber profile database to the recently frequently occurring factory equilibrium moisture content value (e.g., 12%) and its reasonable fluctuation range. Updating the timber profile data for the corresponding timber species enables the system's quality control standards to self-evolve, no longer rigidly adhering to the initially set theoretical parameters, but dynamically adjusting the qualification standards based on actual production results and statistical patterns. This makes quality control more aligned with actual production and market demands, thereby fundamentally reducing the number of timbers misjudged as "abnormal" in the future, improving production efficiency and product adaptability. By analyzing macro-level data trends, systemic deviations are identified, and the core knowledge base (wood profile) is optimized in reverse. This represents a shift from "passive detection and control" to "proactive prediction and optimization," forming a truly intelligent and continuously evolving production management closed loop. Abnormal wood refers to wood whose factory equilibrium moisture content exceeds the ideal equilibrium moisture content range, as determined by the backend server. This type of wood indicates substandard drying quality and may pose a risk of subsequent cracking and deformation. The preset cycle refers to the time window used by the backend server to statistically analyze the frequency of anomalies, such as a week, a month, or a batch production cycle. The anomaly percentage threshold is a pre-set percentage by the backend server used to determine whether the production quality of a certain type of wood has transitioned from an occasional anomaly to a systemic problem.Timber profile data refers to the set of core parameters related to a particular timber species stored in the timber profile database, including preset moisture content ranges, ideal moisture content gradient ranges, and ideal equilibrium moisture content ranges.

[0084] These embodiments enhance the intelligence level, process controllability, and product quality stability of timber production. Specifically, the system first receives and analyzes moisture content change graphs to quickly pinpoint abnormal process steps. By comparing the real-time environment and process parameters of this step with massive historical data, the system can intelligently determine whether the root cause of the problem stems from a malfunction in temperature and humidity equipment or inaccurate process parameter settings, and generates targeted maintenance instructions (temperature and humidity equipment detection instructions or process step detection instructions). This guides maintenance personnel to conduct precise troubleshooting, greatly improving the efficiency of anomaly handling and avoiding resource waste caused by blind adjustments. Furthermore, through dynamic statistics and trend analysis of the proportion of abnormal timber entering the warehouse, the system can adaptively update timber profile data, ensuring that process control standards remain dynamically consistent with actual production. This effectively addresses differences in raw material characteristics and changes in new market demands, fundamentally preventing the recurrence of similar quality problems. Thus, the production quality control system has achieved a leap from "static execution" to "dynamic optimization," significantly improving the intelligence level, process controllability, and product quality stability of timber production.

[0085] In some embodiments, to further address the third technical problem described in the background section, namely, "existing technologies in the quality control of wood products adopt static and universal factory standards, failing to implement dynamic adaptation and regulation based on the differences in end-use environments, leading to quality deterioration of products during circulation and use due to environmental mismatch, making it difficult to guarantee delivery reliability," in some embodiments of the present invention, a big data-based intelligent control method for production processes further includes:

[0086] Step 1: Determine the sales location number based on the order number; determine the equilibrium moisture content of the sales location based on the sales location number, timber type number, and current time.

[0087] In some embodiments, such as Figure 3The diagram illustrates a sales location adaptation and shipping decision-making process flowchart for a big data-based intelligent control method for production processes according to the present invention. The backend server, based on the order number that triggered the current processing flow, queries the order database for the destination information bound to that order and converts it into a standardized sales location number (e.g., "SZX" represents Shenzhen, "BJ" represents Beijing). This number uniquely identifies the final location where the timber will be transported and used. The backend server combines the sales location number, the timber type number of the current timber, and the current time (e.g., the time the server received the instruction, or the planned shipping time) to query an integrated environmental database. This environmental database stores historical and predicted climate data for different geographical regions, seasons, and timber types. The server directly retrieves the equilibrium moisture content of the sales location corresponding to the sales location number and the current season. This is the expected stable moisture content that the timber can reach after long-term storage in the local natural environment. As an example, the backend server determines that the sales location of the order is Shenzhen (number SZX), the timber is redwood (number HW001), and the current time is September. After querying the environmental database, the "equilibrium moisture content of rosewood in Shenzhen in September" was found to be 15%. The equilibrium moisture content at the sales location refers to the moisture content that wood will eventually reach after long-term storage in the specific climate environment (specific temperature and humidity conditions) corresponding to a particular sales location number, and which will not change further. It is an expected or typical value calculated based on historical climate data.

[0088] Step 2: Compare the factory equilibrium moisture content with the sales location equilibrium moisture content. If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is less than a preset difference threshold, a shipping instruction will be generated.

[0089] Step 3: If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is greater than or equal to the preset difference threshold but less than the critical value, a pre-shipment calibration and control instruction is generated.

[0090] In some embodiments, the preset difference threshold is a pre-defined maximum moisture content difference that allows timber to be shipped directly. It defines a "safe zone" within which the timber is considered to be adapted to the sales location environment without adjustment. Based on this, the backend server subtracts the sales location equilibrium moisture content from the actual factory equilibrium moisture content of the timber, calculating the absolute difference between the two. When the absolute difference is determined to be less than the preset difference threshold, a shipping instruction is generated. The shipping instruction is a control command generated by the backend server, indicating that the timber has passed all quality inspections and sales location adaptability checks and can be arranged for shipment.

[0091] In some embodiments, the critical value is another pre-defined moisture content difference limit that is larger than the preset difference threshold. The preset difference threshold and the critical value together define a "control zone." When the moisture content difference is greater than or equal to the preset difference threshold but less than the critical value, the timber enters the control zone. This control zone indicates a significant difference, posing a risk to direct shipment, but which can be corrected through rapid in-plant adjustments. When the difference is determined to be within the "control zone," a pre-shipment calibration control instruction is generated. This pre-shipment calibration control instruction is a control command generated by the backend server, requiring the timber to undergo final moisture content calibration treatment (such as rapid humidification or drying) to bring its moisture content close to the equilibrium moisture content at the sales location, ensuring marketability.

[0092] Step 4: If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is greater than or equal to the critical value, a prohibition on shipment instruction is generated, and temperature and humidity adjustment information for the production workshop at the sales location is generated; based on the timber type number, the remaining sales location numbers are matched; if the match is successful, a transfer shipment instruction is generated; if the match fails, an inventory instruction is generated.

[0093] Step 5: Send the delivery order, pre-shipment calibration and control order, transfer delivery order, prohibition of delivery order, or inventory order to the cargo management terminal so that the corresponding management personnel at the cargo management terminal can control the delivery.

[0094] In some embodiments, when the difference is greater than or equal to a critical value, the backend server automatically creates a structured data record. This record includes the instruction type (prohibit shipment), target order number, timber RFID tag, reason for judgment (moisture content difference exceeds critical value), and timestamp, and is stored in the instruction database for distribution. The prohibition on shipment instruction is a control instruction generated by the backend server that explicitly prohibits the timber from being sent to the original sales location to prevent inevitable quality loss. Simultaneously, the backend server invokes a data analysis service to generate a diagnostic report (i.e., production workshop temperature and humidity adjustment information) based on the problematic sales location number, timber type number, and abnormal moisture content difference, combined with historical process data. This report exists in document or data message form, containing the recommended target range for temperature and humidity adjustment, the affected workshop or process, and is automatically sent to the production management subsystem or the work interface of relevant personnel to guide long-term process optimization. Based on this, the backend server executes a database query and comparison algorithm. Using the current timber species number and factory equilibrium moisture content as key query conditions, it iterates through all other unfulfilled orders or potential sales locations in the order database or sales location environment database. It calculates the difference between the equilibrium moisture content of the sales location and the current timber moisture content, filtering out sales location numbers whose difference is less than a preset threshold (i.e., the remaining sales location numbers). When the matching algorithm returns a successful sales location number, the backend server creates a new instruction (i.e., a transfer and shipment instruction). This instruction includes the raw timber information, the new target sales location number, the new order number, and instructs the logistics system to update the shipping destination. Simultaneously, the backend server automatically associates the new order with the RFID tag of the raw timber. When the matching algorithm returns a failure result (no suitable sales location found), the backend server creates an inventory instruction. This instruction includes the timber RFID tag, updates the timber's status in the inventory management database to "pending warehousing," assigns it a storage location, and guides warehouse personnel to move it to the designated area. The temperature and humidity adjustment information in the production workshop is a feedback message generated by the backend server (not an immediate instruction). It indicates that the current production process (especially the control target for the equilibrium moisture content at the factory outlet) is severely mismatched with the environmental requirements of the original sales location. It recommends that the process department review and adjust the temperature and humidity control standards of the relevant workshops. This is feedback for long-term process optimization. Based on this, the backend server proactively pushes these instruction data packets (i.e., shipping instructions, pre-shipment calibration and adjustment instructions, transfer shipping instructions, prohibition of shipping instructions, or inventory instructions) to pre-configured goods management terminals through the enterprise's internal network (such as message queues). After receiving the instructions from the backend server, the goods management terminal will prompt the administrator through highlighting, pop-up windows, or sound on its graphical user interface.Based on the specific instruction type and its detailed information displayed on the screen, the administrator performs corresponding operations in the physical world, such as confirming loading, guiding timber to the calibration area, changing shipping labels, suspending the shipping process, or moving timber to a designated storage location. After the administrator confirms the operation on the terminal, the terminal feeds back the execution result to the backend server, forming an operational loop. The cargo management terminal refers to the intelligent terminal device (such as a computer or handheld device) used by warehouse management or logistics dispatch personnel. It is responsible for receiving the final shipping decision instructions from the backend server.

[0095] This includes generating pre-shipment calibration and control instructions, including:

[0096] Determine the latest delivery date based on the order number and calculate the adjustment period;

[0097] Determine if the sales location code corresponds to a northern city and whether the current time is during the heating season;

[0098] If so, the target adjusted moisture content value is calculated based on the preset floating ratio and the equilibrium moisture content of the heating room corresponding to the sales location number; the target adjusted moisture content value, adjustment period, order number and timber type number are packaged in the pre-shipment calibration and control instruction;

[0099] If not, the target moisture content adjustment range is calculated based on the preset floating ratio and the equilibrium moisture content at the sales location; the target moisture content adjustment range, adjustment period, order number, and timber type number are then packaged into a pre-shipment calibration and control instruction.

[0100] In some embodiments, the backend server queries the order database using the order number as an index to retrieve the "latest delivery date" stipulated in the contract. Then, the backend server compares the current time with the "latest delivery date" to calculate the remaining days, which is the adjustment period. Based on this, the backend server queries the regional attribute database according to the sales location number to determine if it is marked as a "northern city." Simultaneously, based on the current time, it determines whether it falls within the preset "heating season" time interval for that region (e.g., the heating season in Beijing is typically from November 15th to March 15th of the following year). If both conditions are "yes," processing logic for the special environment of "heated rooms" is triggered. If the determination is yes (northern city and during the heating season), the backend server queries the climate database for the equilibrium moisture content of the heated room corresponding to that sales location number (this is a value far lower than the normal outdoor equilibrium moisture content; for example, it may be as low as 5% inside a heated room in Beijing during winter). Then, based on a preset fluctuation ratio (e.g., 5%), a single target value is calculated. The target adjusted moisture content value is equal to the equilibrium moisture content of the heated room corresponding to the sales location number, multiplied by one, plus a preset floating ratio. (Logically, an upward floating ratio is used here to minimize the risk of wood shrinkage due to water loss in a dry environment). The backend server then encapsulates the calculated target adjusted moisture content value (a specific value, such as 5.25%), the adjustment period, the order number, and the wood type number into a structured data package, i.e., a complete pre-shipment calibration and control instruction. The latest delivery date refers to the final delivery time agreed upon in the order, used to constrain the completion time of the wood control process. The adjustment period refers to the allowed process adjustment time from the current time to the latest delivery date, used to guide the production workshop in rationally arranging humidification, drying, and other control operations. The sales location number is a unique identifier assigned by the backend server to different sales locations, used to quickly locate the target sales location during data retrieval and matching. The heating season refers to the period during which centralized heating is provided in northern cities during winter. During this period, the indoor air humidity at the sales location differs from the normal environment, thus affecting the benchmark for determining the equilibrium moisture content of the wood. The preset floating ratio refers to the upper and lower fluctuation range of moisture content adjustment set for different sales environment, used to avoid excessive shrinkage or expansion of timber during transportation or use. The equilibrium moisture content of a heated room refers to the long-term stable moisture content value that timber can achieve in a typical heated room environment during the heating season in northern cities. The target adjusted moisture content value is a single target moisture content value calculated by combining the preset floating ratio and the equilibrium moisture content of the heated room, used to guide the production workshop in performing targeted adjustments.

[0101] In some embodiments, if the above determination is negative (non-northern city, or northern city but not in the heating season), the backend server directly uses the normal equilibrium moisture content at the sales location (e.g., 15%). A target range is calculated based on a preset fluctuation ratio (e.g., 5%). The target adjusted moisture content range is a range. Its lower limit is equal to the sales location equilibrium moisture content multiplied by one minus the preset fluctuation ratio; its upper limit is equal to the sales location equilibrium moisture content multiplied by one plus the preset fluctuation ratio. For example, the range is [14.25%, 15.75%]. Subsequently, the backend server encapsulates the calculated target adjusted moisture content range, adjustment period, order number, and timber type number into a pre-shipment calibration and control instruction. The target adjusted moisture content range is an acceptable range calculated by combining the preset fluctuation ratio and the sales location equilibrium moisture content, used to provide a certain moisture content control margin for the production workshop. The sales location equilibrium moisture content is the stable moisture content value of the target sales location under normal environmental conditions. Among them, the pre-shipment calibration and control instruction is a control information generated by the back-end server and sent to the process maintenance terminal. It is used to prompt workshop operators to adjust the moisture content value or the moisture content range according to the target. Within the adjustment period, the target wood is humidified, dried again or other moisture content control operations are performed to ensure that the wood leaving the factory meets the usage requirements of the target sales location.

[0102] These embodiments improve product quality and delivery reliability. Specifically, the equilibrium moisture content of timber at the sales location is predicted based on order information and used as the final quality target for the timber. By comparing the factory equilibrium moisture content with this target, precise control and risk management can be achieved: for timber with moderate differences, a pre-shipment calibration and control instruction is generated, including an adjustment period and a target adjustment moisture content value or range calculated in conjunction with the heating season environment, ensuring that the timber is fine-tuned to the most suitable state for the end-user environment before leaving the factory; for timber with excessive differences, a prohibition on shipment instruction is generated, and at the same time, a transfer shipment instruction or an inventory instruction is generated for other sales locations, separating the risky timber from the original order. Through this three-level linkage control (direct shipment / calibration / prohibition of shipment) based on end-user environment prediction, timber quality control standards are extended from internal factory standards to end-user standards, fundamentally preventing quality defects such as cracking and deformation caused by environmental differences, and significantly improving product delivery reliability, market adaptability, and customer satisfaction.

[0103] The above description is merely a selection of preferred embodiments of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to specific combinations of the above-described technical features, but also includes other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A production process intelligent control method based on big data, characterized in that, include: Acquire timber information, including timber species number and order number; determine the preset probe coarse measurement point location information based on the timber species number and a pre-stored timber profile database. The pre-stored timber profile database includes multiple timber species numbers, a preset moisture content range corresponding to each timber species number, an ideal moisture content gradient range, and the preset probe coarse measurement point location information. The probe coarse measurement point location information is sent to the coarse measurement terminal so that the testing personnel corresponding to the coarse measurement terminal can measure the moisture content of the wood and obtain the coarse moisture content. The rough moisture content is received and compared with a preset moisture content range to obtain a first comparison result. The first comparison result is used to characterize the initial adjustment or retesting of the moisture content of the wood. If the first comparison result indicates that the moisture content of the wood needs to be retested, then a fine measurement instruction is sent to the corresponding processing terminal so that the testing personnel at the processing terminal can measure the moisture content of the wood and obtain the fine measurement moisture content. Receive the measured moisture content and determine the actual moisture content gradient; compare the actual moisture content gradient with the ideal moisture content gradient range to obtain a second comparison result, which is used to characterize whether the wood has undergone secondary regulation or is qualified. Based on the first comparison result or the second comparison result, an initial control instruction, a fine measurement instruction, a secondary control instruction, or a timber qualification instruction are generated and sent to the corresponding processing terminal, including: If the measured moisture content is less than or equal to the lower limit of the preset moisture content range, the wood is determined to be over-dried, and the difference between the measured moisture content and the lower limit of the preset moisture content range is calculated and determined as the first difference. If the first difference is greater than or equal to the first preset value, a first scrap instruction is generated. If the first difference is less than the first preset value, a first humidification treatment instruction is generated. If the measured moisture content is greater than or equal to the upper limit of the preset moisture content range, the wood is determined to be not completely dry, and a first and second drying treatment instruction is generated. The first scrapping instruction, the first humidification treatment instruction, or the first secondary drying treatment instruction shall be determined as the initial control instruction. If the coarse moisture content is within the preset moisture content range, a fine measurement command is generated; If the actual moisture content gradient is less than or equal to the lower limit of the ideal moisture content gradient range, the wood is determined to be over-dried, and a second difference between the actual moisture content gradient and the lower limit of the ideal moisture content gradient range is calculated. If the second difference is greater than or equal to a second preset value, a second scrap instruction is generated; if the second difference is less than the second preset value, a second humidification treatment instruction is generated. If the actual moisture content gradient is greater than or equal to the upper limit of the ideal moisture content gradient range, the wood is determined to be not completely dry. The third difference between the actual moisture content gradient and the upper limit of the ideal moisture content gradient range is calculated. If the third difference is greater than or equal to the third preset value, a second scrap instruction is generated. If the third difference is less than the third preset value, a second secondary drying treatment instruction is generated. The second scrapping instruction, the second humidification treatment instruction, or the second secondary drying treatment instruction shall be defined as the secondary control instruction; If the actual moisture content gradient is within the ideal moisture content gradient range, a timber qualification instruction is generated.

2. The intelligent control method for production processes based on big data according to claim 1, characterized in that, The processing terminal includes a fine measurement area processing terminal and a coarse measurement area processing terminal; the initial control command and the fine measurement command are configured with corresponding first color development commands; the secondary control command and the timber qualification command are configured with corresponding second color development commands.

3. The intelligent control method for production processes based on big data according to claim 2, characterized in that, The pre-stored timber profile database also includes the ideal equilibrium moisture content range corresponding to each timber species number, and Receive the pre-process moisture content and post-process moisture content of each of the multiple key processes, and generate a moisture content change graph; The factory equilibrium moisture content of the received timber after it has undergone several key processes; The factory equilibrium moisture content is compared with the ideal equilibrium moisture content range. If the factory equilibrium moisture content exceeds the ideal equilibrium moisture content range, the abnormal time point is located according to the moisture content change graph. The current temperature and humidity of the production workshop corresponding to the abnormal time point are obtained. The key process corresponding to the abnormal time point is selected as the process to be tested. Based on the abnormal time point, query the temperature and humidity of multiple historical production workshops, and count the proportion of first data whose difference rate with the current production workshop temperature and humidity is less than the first preset similarity threshold among the multiple historical production workshop temperature and humidity; if the first data proportion does not reach the first preset ratio, generate a temperature and humidity equipment detection instruction and send it to the equipment maintenance terminal so that the operation and maintenance personnel corresponding to the equipment maintenance terminal can check the abnormal equipment. Based on the process to be tested, multiple historical process parameters are queried, and the proportion of second data in the multiple historical process parameters whose difference rate with the process parameters corresponding to the process to be tested is less than the second preset similarity threshold is calculated. If the proportion of second data does not reach the second preset ratio, a process step detection instruction is generated and sent to the process maintenance terminal so that the maintenance personnel corresponding to the process maintenance terminal can adjust the process parameters.

4. The intelligent control method for production processes based on big data according to claim 3, characterized in that, Also includes: Timber with a factory equilibrium moisture content that exceeds the ideal equilibrium moisture content range is identified as abnormal timber. The percentage of abnormal timber entering the warehouse is statistically analyzed within a preset period. If the percentage of abnormal timber entering the warehouse exceeds the abnormal percentage threshold, the timber profile data of the corresponding timber type is updated.

5. The intelligent control method for production processes based on big data according to claim 4, characterized in that, Also includes: Determine the sales location number based on the order number; determine the corresponding equilibrium moisture content at the sales location based on the sales location number, timber type number, and current time. The factory equilibrium moisture content is compared with the sales location equilibrium moisture content. If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is less than a preset difference threshold, a shipping instruction is generated. If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is greater than or equal to the preset difference threshold but less than the critical value, a pre-shipment calibration and control instruction will be generated.

6. The intelligent control method for production processes based on big data according to claim 5, characterized in that, Also includes: If the difference between the factory equilibrium moisture content and the sales location equilibrium moisture content is greater than or equal to the critical value, a prohibition on shipment instruction is generated, and temperature and humidity adjustment information for the production workshop at the sales location is generated; based on the timber type number, the remaining sales location numbers are matched; if the match is successful, a transfer shipment instruction is generated; if the match fails, an inventory instruction is generated. The delivery instructions, pre-shipment calibration and control instructions, transfer delivery instructions, prohibition of delivery instructions, or inventory instructions are sent to the cargo management terminal so that the corresponding management personnel at the cargo management terminal can control the delivery.

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