Cable production management method and system based on knowledge graph

Abstract

The invention provides a cable production management method and system based on a knowledge graph, belongs to the technical field of data processing, and is used for improving the reliability of cable production management control. The method comprises the steps that a production management instruction of a cable production line is acquired, the production management instruction is a first type of production management instruction or a second type of production management instruction, the first type of production management instruction belongs to a production management instruction indicating improvement of the production line speed, and the second type of production management instruction belongs to a production management instruction indicating reduction of the production line speed; based on the knowledge graph of cable production management, at least one causal chain generated by the production management instruction on a digital twinning model is determined, the digital twinning model is digital twinning of the cable production line, and the at least one causal chain guides production hidden dangers; and determining at least one production management compensation scheme of the digital twin model according to the at least one causal chain.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a cable production management method and system based on knowledge graphs. Background Technology

[0002] Cable production is a complex process industry involving multiple stages such as wire drawing, annealing, stranding, insulation extrusion, cabling, and sheathing. Its production quality and efficiency highly depend on the coordination and stability of hundreds of process parameters (such as production line speed, extruder temperature and pressure, cooling conditions, and unwinding and take-up tension). In actual production, it is common practice to dynamically adjust the production rhythm according to changes in order demand, equipment status, or raw material batches. The most crucial adjustment is the increase or decrease of the overall production line speed.

[0003] However, existing cable production management systems (such as MES and SCADA) or operating modes that rely on human experience are prone to misoperation when faced with dynamic adjustments to production speed. Summary of the Invention

[0004] This application provides a knowledge graph-based cable production management method and system to improve the reliability of cable production management and control.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, a knowledge graph-based cable production management method is provided, applied to electronic devices. The method includes: acquiring production management instructions for the cable production line, wherein the production management instructions are either a first type of production management instruction or a second type of production management instruction. The first type of production management instruction is a production management instruction that instructs to increase the speed of the production line, and the second type of production management instruction is a production management instruction that instructs to decrease the speed of the production line; based on the knowledge graph of cable production management, determining at least one causal chain generated by the production management instructions on a digital twin model, wherein the digital twin model is a digital twin of the cable production line, and the at least one causal chain leads to potential production hazards; and determining at least one production management compensation scheme for the digital twin model based on the at least one causal chain.

[0006] Optionally, the knowledge graph includes multiple if rules, and there are dependencies between the multiple if rules. Based on the knowledge graph of cable production management, determining at least one causal chain generated by production management instructions on the digital twin model includes: activating the first if rule among the multiple if rules using the production management instructions; activating at least one if rule sequentially based on the dependencies between the multiple if rules when the first if rule is activated; and determining at least one causal chain during the process of activating the first if rule and at least one if rule sequentially.

[0007] Optionally, when the production management instruction is a first-type production management instruction, multiple if rules include: Rule R1: If the production line speed V increases and the cooling water tank length L remains unchanged, the conductor's residence time T in the cooling water tank decreases; Rule R2: If the residence time T decreases and falls below the critical value T_critical, the insulation layer is not sufficiently cooled; Rule R3: If the insulation layer is not sufficiently cooled, the insulation layer crystallinity X decreases; Rule R4: If the crystallinity X decreases, the product dielectric strength Y decreases, and the failure risk Z increases; Rule R5: If the production line speed V increases, the upstream wire tension F needs to be adjusted to maintain wire diameter stability; the first if rule and at least one if rule are activated sequentially, including: according to the production management instruction, activating rule R1, resulting in fact 1, where fact 1 is a decrease in residence time T; according to fact... Based on the dependency relationship between Fact 1 and Rule R2, Rule R2 is activated, resulting in Fact 2, which is insufficient cooling of the insulation layer. Based on the dependency relationship between Fact 2 and Rule R3, Rule R3 is activated, resulting in Fact 3, which is a decrease in crystallinity X. Based on the dependency relationship between Fact 3 and Rule R4, Rule R4 is activated, resulting in Fact 4, which is a decrease in product dielectric strength Y and an increase in failure risk Z. Based on the production management instruction, Rule R5 is activated, resulting in Fact 5, which is that the upstream wire tension F needs to be adjusted. Based on Facts 2, 4, and 5, at least one causal chain is determined to include: Causal chain 1: Insufficient residence time of the conductor in the cooling water tank due to increased production line speed; Causal chain 2: Increased failure risk Z due to increased production line speed; Causal chain 3: Adjustment of upstream wire tension F due to increased production line speed.

[0008] Optionally, in rules R1, R2, R3, R4 and R5, rules R2 and R4 are encrypted rules. Activating rule R2 includes: when rule R1 is activated, taking the root key built into the electronic device, the feature field in the activated rule R1 and the count value of counter 1 as input parameter 1, and using elliptic curve cryptography to calculate input parameter 1 to generate temporary key 1 and temporary private key 1. Decrypt and activate the encrypted rule R2 using temporary public key 1; with rule R3 activated, update the counter 1 by 1, then use the root key built into the electronic device, the feature field in the activated rule R1, and the counter 1 as input parameter 2, and use elliptic curve cryptography to calculate input parameter 2 to generate temporary key 2 and temporary private key 2; encrypt rule R2 again using temporary private key 2; activating rule R4 includes: with rule R3 activated, using the root key built into the electronic device, the feature field in the activated rule R3, and the counter 2 as input parameter 3, and use elliptic curve cryptography to calculate input parameter 3 to generate temporary key 3 and temporary private key 3; decrypt and activate the encrypted rule R4 using temporary public key 3; with rule R5 activated, update the counter 2 by 1, then use the root key built into the electronic device, the feature field in the activated rule R4, and the counter 2 as input parameter 4, and use elliptic curve cryptography to calculate input parameter 4 to generate temporary key 4 and temporary private key 4; encrypt rule R4 again using temporary private key 4.

[0009] As can be seen, the encryption mechanism is not static but deeply integrated with the inference process. The decryption of rule R2 depends on the successful activation of the preceding rule R1 for its "key" (temporary public key 1). Similarly, the decryption of rule R4 depends on the activation of rule R3. This means that the inference engine can only gain the authority to decrypt and execute the next critical rule when it has legally and correctly executed a specific step. This design effectively prevents rules from being extracted, analyzed, or maliciously triggered in isolation, achieving unprecedented fine-grained security control. Furthermore, a counter is used in the security scheme, and a new temporary private key is used for each re-encryption. This means that the encryption state of a rule changes every time it is used. Therefore, information such as the counter value can be used to trace when and in which inference process a specific rule was invoked, providing a data foundation for process optimization and security auditing. Moreover, even if a generated temporary key is cracked, the rule has been re-encrypted with a new temporary private key, preventing attackers from decrypting historical or subsequent versions of the rule, greatly enhancing the long-term security of core process knowledge.

[0010] Optionally, when the production management instruction is a type II production management instruction, the multiple if rules include: Rule Ra: If the production line speed V decreases and the cooling water tank length L remains unchanged, the residence time T of the conductor in the cooling water tank increases; Rule Rb: If the residence time T increases and falls below the critical value T_max, the insulation layer is overcooled; Rule Rc: If the insulation layer is overcooled, the insulation material becomes brittle and its impact resistance decreases; the first if rule and at least one if rule are activated sequentially, including: according to the production management instruction, rule Ra is activated to obtain fact a, fact a is an increase in residence time T; according to the dependency relationship between fact a and rule Rb, rule Rb is activated to obtain fact b, fact b is overcooling of the insulation layer; according to the dependency relationship between fact b and rule Rc, rule Rc is activated to obtain fact c, fact c is brittleness of the insulation material; according to fact 3, at least one causal chain is determined to include: causal chain a: the insulation material becomes brittle because the production line speed decreases.

[0011] Optionally, among rules Ra, Rb, and Rc, rule Rb is the encrypted rule. Activating rule Rb includes: when rule Ra is activated, using the root key built into the electronic device, the feature field in the activated rule Ra, and the count value of counter 3 as input parameter 5, and using elliptic curve cryptography to calculate input parameter 5 to generate temporary key 5 and temporary private key 5; decrypting and activating the encrypted rule Rb using temporary public key 5; when rule Rc is activated, updating the count value of counter 3 by 1, then using the root key built into the electronic device, the feature field in the activated rule Ra, and the count value of counter 3 as input parameter 6, and using elliptic curve cryptography to calculate input parameter 6 to generate temporary key 6 and temporary private key 6; and encrypting rule Rb again using temporary private key 6.

[0012] As can be seen, its encryption mechanism is also tightly coupled with the inference chain (Ra→Rb→Rc). The decryption of rule Rb depends on the activation of rule Ra, while its subsequent encryption is related to the activation of rule Rc. This constructs a complete security closed loop for the independent inference path of "deceleration → overcooling → material embrittlement," ensuring that expert knowledge for the deceleration scenario remains under control throughout storage and use. Furthermore, unlike the above, it uses an independent counter 3, indicating that the security encryption sequences for the inference chains of the two different operating conditions, "speed increase" and "speed decrease," are independent of each other. This design allows the system to configure and manage independent security contexts for different production modes, avoiding key confusion in different scenarios and enhancing the overall security and management flexibility of the system. It demonstrates that the security scheme is not a one-size-fits-all approach, but rather adapted to different process logics in various scenarios.

[0013] Optionally, based on at least one causal chain, at least one production management compensation scheme for the digital twin model is determined, including: inputting at least one causal chain into the digital twin model to obtain the verification result of at least one causal chain output by the digital twin model; and generating a compensation hypothesis scheme if the verification result of at least one causal chain is valid.

[0014] Optionally, the digital twin model includes at least one of the following: a motion model, a mechanical model, a thermodynamic model, or a crystallization kinetic model.

[0015] Secondly, a knowledge graph-based cable production management method system is provided for use in electronic devices, the system being configured to perform the method described in the first aspect.

[0016] Thirdly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer causes the computer to perform the method described in the first aspect.

[0017] In summary, the above methods and systems have the following technical effects: By forcibly utilizing knowledge graphs and digital twin models to perform deductions before executing any speed change commands, all causal chains that could potentially lead to "production hazards" are identified in advance. This allows the system to discover and incorporate potential problems into the processing flow before they actually occur, turning a reactive approach into a proactive one. This fundamentally eliminates batch quality accidents caused by improper parameter adjustments, significantly improving the stability and reliability of production management. Furthermore, this classification ensures that technical solutions can be triggered and executed in both acceleration and deceleration scenarios. The system can adaptively handle potential problems with opposite directions and different physical natures (e.g., speeding up may lead to insufficient cooling, while deceleration may lead to overcooling) and generate appropriate compensation solutions. This overcomes the limitations of traditional systems or human experience that may focus only on one type of working condition while ignoring another, achieving intelligent coverage and adaptive management of the entire scenario for dynamic adjustment of cable production speed. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the knowledge graph-based cable production management method provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0020] This application will present various aspects, embodiments, or features relating to systems that may include multiple devices, components, modules, etc. It should be understood and appreciated that individual systems may include additional devices, components, modules, etc., and / or may not include all the devices, components, modules, etc. discussed in conjunction with the accompanying drawings. Furthermore, combinations of these approaches are also possible.

[0021] Furthermore, in the embodiments of this application, the words "exemplary," "for example," etc., are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the term "exemplary" is intended to present the concept in a concrete manner.

[0022] In the embodiments of this application, the terms "information," "signal," "message," "channel," and "singaling" may sometimes be used interchangeably. It should be noted that, without emphasizing their distinction, their intended meanings are consistent. Similarly, "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing their distinction, their intended meanings are consistent. Furthermore, the " / " mentioned in this application can be used to indicate an "or" relationship.

[0023] The network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0024] For example, Figure 1 This application provides a schematic flowchart of a knowledge graph-based cable production management method. This method can be applied to electronic devices.

[0025] like Figure 1 As shown, the process of this knowledge graph-based cable production management method is as follows: S101, Electronic equipment receives production management instructions from the cable production line.

[0026] Production management instructions are categorized into Category I or Category II. Category I instructions instruction increase the production line speed, while Category II instructions instruction decrease the production line speed. Electronic equipment can obtain these production management instructions from the management terminal of the administrator.

[0027] S102, an electronic device based on a knowledge graph of cable production management, identifies at least one causal chain generated by production management instructions on a digital twin model.

[0028] At least one causal chain leads to potential production hazards.

[0029] A knowledge graph can include multiple if rules, and there are dependencies between multiple if rules.

[0030] For example, case 1: When the production management instruction is a Type I production management instruction (e.g., production rate increased by 15%), several if rules may include: Rule R1: If the production line speed V increases and the cooling water tank length L remains unchanged, the conductor's residence time T in the cooling water tank decreases; Rule R2: If the residence time T decreases and falls below the critical value T_critical, the insulation layer is not sufficiently cooled; Rule R3: If the insulation layer is not sufficiently cooled, the insulation layer crystallinity X decreases; Rule R4: If the crystallinity X decreases, the product dielectric strength Y decreases, and the failure risk Z increases; Rule R5: If the production line speed V increases, the upstream wire tension F needs to be adjusted to maintain wire diameter stability.

[0031] For example, case 2: When the production management instruction is a type II production management instruction (such as a 15% reduction in production rate), multiple if rules may include: Rule Ra: If the production line speed V decreases and the cooling water tank length L remains unchanged, the residence time T of the conductor in the cooling water tank increases; Rule Rb: If the residence time T increases and falls below the critical value T_max, the insulation layer is overcooled; Rule Rc: If the insulation layer is overcooled, the insulation material becomes brittle and its impact resistance decreases.

[0032] Based on this, for S102, the electronic device can perform the following steps 1-3.

[0033] Step 1: Electronic devices can use production management instructions to activate the first of multiple if rules.

[0034] Step 2: If the first if rule is activated, the electronic device can sequentially activate at least one if rule based on the dependency relationship between multiple if rules.

[0035] Specifically, regarding scenario 1: The first if rule and at least one if rule are activated sequentially, including: the electronic device can activate rule R1 according to the production management instruction to obtain fact 1, fact 1 being a reduction in dwell time T; then, the electronic device can activate rule R2 according to the dependency relationship between fact 1 and rule R2 to obtain fact 2, fact 2 being insufficient cooling of the insulation layer; then, the electronic device can activate rule R3 according to the dependency relationship between fact 2 and rule R3 to obtain fact 3, fact 3 being a reduction in crystallinity X; then, the electronic device can activate rule R4 according to the dependency relationship between fact 3 and rule R4 to obtain fact 4, fact 4 being a reduction in product dielectric strength Y and an increase in failure risk Z; and the electronic device can also activate rule R5 according to the production management instruction to obtain fact 5, fact 5 being that the upstream wire tension F needs to be adjusted.

[0036] Optionally, among rules R1, R2, R3, R4, and R5, rules R2 and R4 are encrypted rules.

[0037] Therefore, activating rule R2 includes: when rule R1 is activated, the electronic device can use its built-in root key, the feature field of the activated rule R1 (such as the character at a preset position in rule R1), and the count value of counter 1 as input parameter 1, and use elliptic curve cryptography to calculate input parameter 1 to generate temporary key 1 and temporary private key 1; the electronic device can use temporary public key 1 to decrypt and activate the encrypted rule R2. Afterwards, when rule R3 is activated, the electronic device can update the count value of counter 1 by incrementing it by 1. Then, the electronic device can use its built-in root key, the feature field of the activated rule R1, and the count value of counter 1 as input parameter 2, and use elliptic curve cryptography to calculate input parameter 2 to generate temporary key 2 and temporary private key 2. Thus, the electronic device can use temporary private key 2 to encrypt rule R2 again.

[0038] Furthermore, activating rule R4 includes the following: When rule R3 is activated, the electronic device can use its built-in root key, the feature field of the activated rule R3 (such as the character at a preset position in rule R3), and the count value of counter 2 as input parameter 3, and use elliptic curve cryptography to calculate input parameter 3 to generate temporary key 3 and temporary private key 3. The electronic device can then use temporary public key 3 to decrypt and activate the encrypted rule R4. Afterwards, when rule R5 is activated, the electronic device can update the count value of counter 2 by 1, and then use its built-in root key, the feature field of the activated rule R4, and the count value of counter 2 as input parameter 4, and use elliptic curve cryptography to calculate input parameter 4 to generate temporary key 4 and temporary private key 4. Finally, the electronic device can use temporary private key 4 to encrypt rule R4 again.

[0039] Regarding situation 2: The first if rule and at least one if rule are activated sequentially, including: the electronic device can activate rule Ra according to the production management instruction to obtain fact a, fact a is an increase in dwell time T; then, the electronic device can activate rule Rb according to the dependency relationship between fact a and rule Rb to obtain fact b, fact b is overcooling of the insulation layer; then, the electronic device can activate rule Rc according to the dependency relationship between fact b and rule Rc to obtain fact c, fact c is brittleness of the insulation material.

[0040] Optionally, among rules Ra, Rb, and Rc, rule Rb is the encrypted rule. Activating rule Rb includes: when rule Ra is activated, the electronic device can use its built-in root key, the feature field of the activated rule Ra (such as the character at a preset position in rule Ra), and the count value of counter 3 as input parameter 5, and use elliptic curve cryptography to calculate input parameter 5 to generate temporary key 5 and temporary private key 5; the electronic device can use temporary public key 5 to decrypt and activate the encrypted rule Rb. Afterwards, when rule Rc is activated, the electronic device can update the count value of counter 3 by incrementing it by 1, and then use the built-in root key, the feature field of the activated rule Ra, and the count value of counter 3 as input parameter 6, and use elliptic curve cryptography to calculate input parameter 6 to generate temporary key 6 and temporary private key 6; finally, the electronic device can use temporary private key 6 to encrypt rule Rb again.

[0041] Step 3: The electronic device can determine at least one causal chain during the process of the first if rule and at least one if rule being activated in sequence.

[0042] For example, regarding scenario 1: Based on facts 2, 4, and 5, the electronic device can determine at least one causal chain including: causal chain 1: the conductor's residence time in the cooling water tank is insufficient due to the increase in production line speed; causal chain 2: the failure risk Z increases due to the increase in production line speed; and causal chain 3: the upstream wire tension F needs to be adjusted due to the increase in production line speed.

[0043] For example, regarding situation 2: Based on fact 3, the electronic device can determine at least one causal chain including: causal chain a: the insulation material becomes brittle due to the reduced production line speed.

[0044] S103, the electronic device determines at least one production management compensation scheme for the digital twin model based on at least one causal chain.

[0045] The digital twin model is a digital twin of the cable production line, and the digital twin model includes at least one of the following: a motion model, a mechanical model, a thermodynamic model, or a crystallization kinetics model.

[0046] For S103, the electronic device can input at least one causal chain into the digital twin model to obtain the verification result of at least one causal chain output by the digital twin model; when the verification result of at least one causal chain is valid, a compensation hypothesis scheme is generated, which will be introduced below in combination with the above Case 1 - Case 2.

[0047] Case 1: The electronic device first quantitatively verifies Causal Chain 1.

[0048] For example, the electronic device can calculate based on the motion model. Specifically, the input parameters input into the motion model include: L, V_old, and V_new. Among them, L is the physical length (meters) of the cooling water tank, which is a fixed device parameter and is stored in the device entity attributes of the knowledge base. V_old is the current production line speed (meters per second). V_new is the target production line speed (meters per second), which is determined by the external instruction "increase by 15%", that is, V_new = V_old * 1.15. The calculation formula of the motion model is T = L / V. Substituting L and V_new into the formula can calculate the new residence time T_new. The electronic device can retrieve the "minimum required cooling time" T_critical from the material entity attributes of this cable model in the knowledge base. T_critical is a process parameter预先确定的工艺参数. The electronic device can compare: IF T_new < T_critical THEN trigger the inference conclusion of "insufficient cooling". Thus, Causal Chain 1 is quantitatively verified.

[0049] The electronic device then quantitatively verifies Causal Chain 2.

[0050] It should be noted that there is an unclear expression "预先确定的工艺参数" in the original text, which is translated as "预先确定" here, and you may need to correct it according to the accurate information.For example, an electronic device can perform calculations based on a crystallization kinetics model. The crystallization kinetics model is a model based on heat transfer and material crystallization kinetics, which is relatively complex, but there are mature models available in the art. This model can use the heat exchange theory equation, as well as the classical Avrami equation or its variants. Specifically, the input parameters for the crystallization kinetics model include: T_melt, T_water, T_new, h, and Material_Properties. Among them, T_melt is the initial temperature (°C) of the extruded molten insulating material, T_water is the cooling water temperature (°C), T_new is the residence time (seconds) calculated above, h is the comprehensive heat transfer coefficient (W / m²·K), which is related to materials, water flow rate, etc., and Material_Properties are material-specific parameters, such as specific heat capacity c_p, density ρ, and crystallization kinetics parameters (n, K). The crystallization kinetics model can perform the following calculation process: 1) Calculate the temperature T_final when the conductor leaves the water tank. Specifically, the differential equation can be solved: dT / dt = - (h * A) / (m * c_p) * (T - T_water), where A is the heat dissipation area and m is the mass. Integrating from time t = 0 to t = T_new can obtain the analytical or numerical solution of T_final. For example, T_final = T_water + (T_melt - T_water) * exp( - (h * A) / (m * c_p) * T_new ), where the cooling rate CR can be approximated as: CR ≈ (T_melt - T_final) / T_new. 2) Calculate the crystallinity X. For example, use the Avrami equation: X(t) = 1 - exp( -K * t^n ), where K and n are Avrami constants that depend on the material and cooling temperature (or cooling rate). It should be understood that K and n are not fixed values, and they have a functional relationship with the calculated cooling rate CR or average cooling temperature T_avg, i.e., K = f(CR), n = g(CR). These functional relationships are obtained by fitting the DSC (Differential Scanning Calorimetry) experimental data of this material and are pre-stored in the material entity of the knowledge base. Finally, substituting K, n, and t = T_new into the Avrami equation can calculate the final crystallinity X_new that can be achieved within the residence time T_new. Thus, the electronic device can retrieve the "minimum allowable crystallinity" X_min required for this cable model from the knowledge base, and then compare: IF X_new < X_min THEN trigger the inference conclusion of "crystallinity does not meet the standard", thereby quantitatively verifying the causal chain 2.

[0051] The electronic device ultimately determined the solution to causal chain 3, namely the production management compensation scheme.

[0052] For example, electronic devices can be calculated based on a mechanical model. Specifically, the input parameters of this model include V_new, D, ρ_copper, E, and ε_target. Here, V_new is the new production line speed (m / s), D is the wire diameter (m), ρ_copper is the density of copper (kg / m³), E is the elastic modulus of copper (Pa), and ε_target is the target elongation (micro-strain), a key process parameter used to ensure that the wire does not undergo plastic deformation under appropriate tension. Based on this, increasing the production line speed means that the wire feeding system needs to provide greater acceleration. This force is proportional to the square of the speed, such as the inertial force F_inertia ≈ 0.5 * ρ_copper * (π*(D / 2)^2) * V_new^2 * C (where C is the system inertia coefficient). Therefore, the model can calculate the tension based on the target elongation. According to Hooke's Law, the stress σ = E * ε, the target stress σ_target = E * ε_target, and the cross-sectional area A = π * (D / 2)^2. Thus, the tension based on elongation F_strain = σ_target * A = E * ε_target * π * (D / 2)^2. Finally, the model can calculate the overall theoretical optimal tension, such as F_optimal = F_strain + F_inertia. It should be understood that this is a simplified model; more complex implementations will consider friction, pulley effects, etc.

[0053] At this point, the electronic device obtains a wire tension setting value F_optimal calculated for the new line speed V_new and optimized by the mechanical model, which constitutes the solution for causal chain 3.

[0054] Scenario 2: The electronic device can calculate the new residence time T_new, similar to case 1 above, which can be understood by referring to it. The electronic device can retrieve the "maximum safe cooling time T_max" allowed by the currently produced cable insulation material from the knowledge base. This parameter is provided by the material supplier or pre-determined through laboratory thermal analysis experiments (such as DSC) and stored in the knowledge base. The electronic device can compare: IF T_new > T_max THEN, then trigger the "overcooling risk" flag and initiate the compensation scheme generation process. Then, the electronic device can generate and verify the compensation scheme (its core is the reverse cooling water temperature). Specifically, the electronic device can generate the hypothesis that "increasing the cooling water temperature reduces the cooling intensity, so that the cable's outlet temperature reaches the safety target T_target within the extended residence time T_new." Then, the electronic device can use a thermodynamic model for calculations. This model has the following predefined formula: T_final = T_water + (T_initial - T_water) *exp( -k * T_new), where T_final is the temperature of the conductor / insulation layer when it leaves the water tank (i.e., the model output, which we aim to make equal to T_target), T_water is the cooling water temperature (this is the variable that needs to be solved in reverse), T_initial is the initial temperature of the insulation layer before entering the water tank (from the extruder temperature model output or obtained from a sensor), and k is the comprehensive heat exchange coefficient, which is related to the material's thermophysical properties, cable structure, water flow velocity, etc. (a pre-calibrated constant). The model can perform inverse kinematics calculations. For example, the above model formula can be rewritten in the form of T_water as an unknown: T_target = T_water + (T_initial - T_water) * exp( -k * T_new ), and then the equation can be solved: T_water_new = (T_target - T_initial * exp( -k * T_new )) / (1 - exp( -k * T_new)). At this point, T_target, T_initial, k, and T_new can be substituted into the above formula to directly calculate T_water_new. The electronic device can check whether the calculated T_water_new is within the physical capability range of the cooling system (e.g., between ambient temperature and 60°C). To ensure accuracy, T_water_new and T_new can be substituted back into the model to verify whether the output T_final approximates T_target infinitely. If so, the cooling water tank temperature setpoint can be adjusted from the [current value] to T_water_new °C.

[0055] In summary, by forcibly utilizing knowledge graphs and digital twin models to perform deductions before executing any speed change commands, all causal chains that could potentially lead to "production hazards" are identified in advance. This allows the system to discover and incorporate potential problems into the processing flow before they actually occur, turning a reactive approach into a proactive one. This fundamentally eliminates batch quality accidents caused by improper parameter adjustments, significantly improving the stability and reliability of production management. Furthermore, this classification ensures that technical solutions can be triggered and executed in both acceleration and deceleration scenarios. The system can adaptively handle potential problems with opposite directions and different physical natures (e.g., speeding up may lead to insufficient cooling, while deceleration may lead to overcooling) and generate appropriate compensation solutions. This overcomes the limitations of traditional systems or human experience that may focus only on one type of operating condition while ignoring another, achieving intelligent coverage and adaptive management of the entire scenario for dynamic adjustment of cable production speed.

[0056] The above combination Figure 1 This application provides a detailed description of a knowledge graph-based cable production management method. The following details a visual neural network-based contact point detection device for a pin-feed connector, used to execute the knowledge graph-based cable production management method provided in this application. This device is applied to electronic devices and configured to perform the above-described... Figure 1 The method shown.

[0057] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example, a knowledge graph-based cable production management system can be applied to this electronic device, which can be a terminal device, or a chip (system) or other component or assembly that can be set in the terminal device. Figure 2 As shown, the electronic device 400 may include a processor 401. Optionally, the electronic device 400 may also include a memory 402 and / or a transceiver 403. The processor 401 is coupled to the memory 402 and the transceiver 403, for example, they can be connected via a communication bus. Alternatively, the electronic device 400 may also be a chip, such as including the processor 401; in this case, the transceiver may be the chip's input / output interface.

[0058] The following is combined with Figure 2 The various components of electronic device 400 are described in detail below: The processor 401 is the control center of the electronic device 400. It can be a single processor or a collective term for multiple processing elements. For example, the processor 401 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0059] Optionally, the processor 401 can perform various functions of the electronic device 400, such as the aforementioned functions, by running or executing software programs stored in the memory 402 and calling scientific data stored in the memory 402. Figure 1 The cable production management method based on knowledge graphs is shown.

[0060] In a specific implementation, as one example, processor 401 may include one or more CPUs, for example... Figure 2 CPU0 and CPU1 are shown in the diagram.

[0061] In a specific implementation, as one example, the electronic device 400 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process scientific data (such as computer programs or instructions).

[0062] The memory 402 is used to store the software program that executes the solution of this application, and is controlled by the processor 401 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0063] Optionally, the memory 402 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or scientific data structures and accessible by a computer, but not limited thereto. The memory 402 may be integrated with the processor 401 or may exist independently and be accessible through the interface circuit of the electronic device 400. Figure 2 (Not shown in the image) is coupled to processor 401, but this embodiment does not specifically limit this.

[0064] Transceiver 403 is used for communication with other electronic devices. For example, if electronic device 400 is a terminal device, transceiver 403 can be used to communicate with a network device or with another terminal device. As another example, if electronic device 400 is a network device, transceiver 403 can be used to communicate with a terminal device or with another network device.

[0065] Alternatively, transceiver 403 may include a receiver and a transmitter. Figure 2 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0066] Alternatively, the transceiver 403 can be integrated with the processor 401, or it can exist independently and be connected via the interface circuit of the electronic device 400. Figure 2 (Not shown in the image) is coupled to processor 401, but this embodiment does not specifically limit this.

[0067] Understandable, Figure 2 The structure of the electronic device 400 shown does not constitute a limitation on the electronic device. Actual electronic devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0068] Furthermore, the technical effects of the electronic device 400 can be referred to the technical effects of the methods described in the above method embodiments, and will not be repeated here.

[0069] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0070] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0071] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or scientific data center to another website, computer, server, or scientific data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a scientific data storage device such as a server or scientific data center that contains one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0072] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0073] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0074] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0075] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0076] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0078] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0080] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A cable production management method based on knowledge graphs, characterized in that, Applied to electronic devices, the method includes: Obtain production management instructions from the cable production line, wherein the production management instructions are either a first type of production management instruction or a second type of production management instruction, wherein the first type of production management instruction is a production management instruction that instructs to increase the speed of the production line, and the second type of production management instruction is a production management instruction that instructs to decrease the speed of the production line; Based on the knowledge graph of cable production management, at least one causal chain is identified in the digital twin model that generates production management instructions. The digital twin model is a digital twin of the cable production line, and the at least one causal chain leads to potential production hazards. Based on the at least one causal chain, determine at least one production management compensation scheme for the digital twin model.

2. The method according to claim 1, characterized in that, The knowledge graph includes multiple if-law rules, and these rules have dependencies on each other. The knowledge graph based on cable production management determines at least one causal chain generated by production management instructions on the digital twin model, including: The production management instruction is used to activate the first of the plurality of if rules; If the first if rule is activated, at least one if rule is activated sequentially based on the dependency relationship between the multiple if rules. The at least one causal chain is determined during the process of the first if rule and the at least one if rule being activated in sequence.

3. The method according to claim 2, characterized in that, When the production management instruction is the first type of production management instruction, the plurality of if rules include: Rule R1: If the production line speed V increases and the cooling water tank length L remains unchanged, the residence time T of the conductor in the cooling water tank decreases; Rule R2: If the residence time T decreases and falls below the critical value T_critical, the insulation layer is not sufficiently cooled; Rule R3: If the insulation layer is not sufficiently cooled, the crystallinity X of the insulation layer decreases; Rule R4: If the crystallinity X decreases, the dielectric strength Y of the product decreases, and the failure risk Z increases; Rule R5: If the production line speed V increases, the upstream wire tension F needs to be adjusted to maintain wire diameter stability. The first if rule and the at least one if rule are activated sequentially, including: According to the production management instruction, rule R1 is activated, resulting in fact 1, which is a reduction in dwell time T; based on the dependency relationship between fact 1 and rule R2, rule R2 is activated, resulting in fact 2, which is insufficient cooling of the insulation layer; based on the dependency relationship between fact 2 and rule R3, rule R3 is activated, resulting in fact 3, which is a decrease in crystallinity X; based on the dependency relationship between fact 3 and rule R4, rule R4 is activated, resulting in fact 4, which is a decrease in product dielectric strength Y and an increase in failure risk Z; and based on the production management instruction, rule R5 is activated, resulting in fact 5, which is that the upstream wire tension F needs to be adjusted. Based on facts 2, 4, and 5, the at least one causal chain is determined to include: causal chain 1: the conductor's residence time in the cooling water tank is insufficient due to the increase in production line speed; causal chain 2: the failure risk Z increases due to the increase in production line speed; and causal chain 3: the upstream wire tension F needs to be adjusted due to the increase in production line speed.

4. The method according to claim 3, characterized in that, In rule R1, rule R2, rule R3, rule R4, and rule R5, rule R2 and rule R4 are encrypted rules, and activating rule R2 includes: When rule R1 is activated, the root key built into the electronic device, the feature field in the activated rule R1, and the count value of counter 1 are used as input parameter 1, and elliptic curve cryptography is used to calculate the input parameter 1 to generate temporary key 1 and temporary private key 1. Use the temporary public key 1 to decrypt and activate the encrypted rule R2; When rule R3 is activated, the count value of counter 1 is updated and incremented by 1. Then, the root key built into the electronic device, the feature field in the activated rule R1, and the count value of counter 1 are used as input parameter 2. Elliptic curve cryptography is used to calculate the input parameter 2 to generate temporary key 2 and temporary private key 2. The rule R2 is re-encrypted using the temporary private key 2; Activating rule R4 includes: When rule R3 is activated, the root key built into the electronic device, the feature field in the activated rule R3, and the count value of counter 2 are used as input parameter 3, and elliptic curve cryptography is used to calculate the input parameter 3 to generate temporary key 3 and temporary private key 3. Use the temporary public key 3 to decrypt and activate the encrypted rule R4; When rule R5 is activated, the count value of counter 2 is updated and incremented by 1. Then, the root key built into the electronic device, the feature field in the activated rule R4, and the count value of counter 2 are used as input parameter 4. Elliptic curve cryptography is used to calculate the input parameter 4 to generate temporary key 4 and temporary private key 4. Rule R4 is re-encrypted using the temporary private key 4.

5. The method according to claim 2, characterized in that, When the production management instruction is the second type of production management instruction, the plurality of if rules include: rule Ra: if the production line speed V decreases and the cooling water tank length L remains unchanged, the residence time T of the conductor in the cooling water tank increases; rule Rb: if the residence time T increases and is below the critical value T_max, the insulation layer is overcooled; rule Rc: if the insulation layer is overcooled, the insulation material becomes brittle and its impact resistance decreases. The first if rule and the at least one if rule are activated sequentially, including: According to the production management instruction, rule Ra is activated to obtain fact a, which is an increase in dwell time T; according to the dependency relationship between fact a and rule Rb, rule Rb is activated to obtain fact b, which is an overcooling of the insulation layer; according to the dependency relationship between fact b and rule Rc, rule Rc is activated to obtain fact c, which is an increase in the brittleness of the insulation material. Based on fact 3, the at least one causal chain is determined to include: causal chain a: the insulation material becomes brittle due to the reduced production line speed.

6. The method according to claim 5, characterized in that, In the rule Ra, the rule Rb, and the rule Rc, the rule Rb is an encrypted rule, and activating the rule Rb includes: When the rule Ra is activated, the root key built into the electronic device, the feature field in the activated rule Ra, and the count value of counter 3 are used as input parameter 5, and elliptic curve cryptography is used to calculate the input parameter 5 to generate temporary key 5 and temporary private key 5. Use the temporary public key 5 to decrypt and activate the encrypted rule Rb; When rule Rc is activated, the count value of counter 3 is updated and incremented by 1. Then, the root key built into the electronic device, the feature field in the activated rule Ra, and the count value of counter 3 are used as input parameter 6. Elliptic curve cryptography is used to calculate the input parameter 6 to generate temporary key 6 and temporary private key 6. The rule Rb is re-encrypted using the temporary private key 6.

7. The method according to any one of claims 1-6, characterized in that, The step of determining at least one production management compensation scheme for the digital twin model based on the at least one causal chain includes: Input the at least one causal chain into the digital twin model to obtain the verification result of the at least one causal chain output by the digital twin model; If the verification result of at least one causal chain is valid, a compensation hypothesis scheme is generated.

8. The method according to claim 7, characterized in that, The digital twin model includes at least one of the following: a motion model, a mechanical model, a thermodynamic model, or a crystallization kinetic model.

9. A cable production management system based on knowledge graphs, characterized in that, Applied to an electronic device, the system is configured to perform the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, include: A computer program or instruction; when the computer program or instruction is run on a computer, it causes the computer to perform the method of any one of claims 1-8.

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