Rat-proof optical cable and production method thereof

Abstract

The invention provides a rat-proof optical cable and a production method thereof, and relates to the field of rat-proof optical cables, and the method comprises the steps: obtaining the environment information of a layout region of the rat-proof optical cable; determining a bite risk value of the layout area according to the environment information of the layout area; determining the thickness of a physical rat-proof layer of an optical cable main body of the rat-proof optical cable according to the bite risk value of the layout area; according to the bite risk value of the layout area, the distance between the bite monitoring devices is determined; the optical cable main body of the rat-proof optical cable is produced according to the thickness of the physical rat-proof layer of the optical cable main body and the distance between the biting monitoring devices; performing biting simulation on the optical cable main body of the rat-proof optical cable to obtain biting simulation data; and according to the biting simulation data, biting monitoring parameters are determined, the biting monitoring parameters are used for instructing the data interaction module to upload data acquired by the biting monitoring unit, the biting monitoring parameters are also used for instructing the cloud to analyze the data acquired by the biting monitoring unit, and the optical cable rat-proof effect is improved.

Description

Technical Field

[0001] The invention relates to the field of rat-proof optical cables, and in particular to a rat-proof optical cable and a production method thereof. Background Art

[0002] With the development of the communications industry, optical fiber cables are being used more and more. Optical cables are usually composed of a loose tube containing optical fibers, a protective layer, and a sheath layer. The protective layer is a steel belt, an aluminum belt, etc., and the sheath layer is generally polyethylene or an improved composite polyolefin.

[0003] At present, the methods of rat prevention for communication optical cables are mainly divided into two categories: physical rat prevention and chemical rat prevention. Physical rat prevention is to increase the hardness of the protective layer of the optical cable to prevent rats. Chemical rat prevention is mainly to add capsaicin to the sheath of the optical cable to prevent rats, and the optical cable produces a repellent odor to prevent rats. Chemical rat prevention is greatly affected by the external environment, and the anti-rat effect is limited, and it has been gradually eliminated. The existing physical rat-proof optical cable is mainly filled with grease metal to strengthen rat prevention. Generally, grease is filled in the gap of the entire cable cross section and steel wire or high-hardness steel belt is used for armored rat prevention. When steel wire is used for rat prevention, when the external object of the steel wire is destroyed, the steel wire will spread along the twisting direction, and the anti-rat ability will be reduced or no anti-rat ability. This type of structural optical cable mostly adopts a central tube structure, and the number of optical cable cores is limited. This method is only used in small-diameter central tube structure optical cables; when high-hardness steel belt armor is used for rat prevention, the cost is high, the cable diameter is large, the weight is large, and it is not easy to construct and lay. In addition, the cross-section grease filling is easy to cause environmental pollution, the grease cleaning is difficult, and it is not easy to weld during construction.

[0004] Therefore, there is a need for a rat-proof optical cable and a production method thereof to improve the rat-proof effect of the optical cable. Summary of the invention

[0005] The present invention provides a rat-proof optical cable, comprising: an optical cable main body, comprising an inner protective layer and an outer protective layer, a plurality of optical cable units are arranged in the inner protective layer, a physical rat-proof layer is arranged between the inner protective layer and the outer protective layer, a gnawing monitoring unit is arranged between the physical rat-proof layer and the inner protective layer, the gnawing monitoring unit comprises a plurality of gnawing monitoring devices, and the spacing between two adjacent gnawing monitoring devices is consistent; a data interaction module arranged on the optical cable main body, used for uploading data collected by the gnawing monitoring unit to a cloud, and the cloud is used for analyzing the data collected by the gnawing monitoring unit; a rat-repelling module, comprising a plurality of rat-repelling components arranged on the optical cable main body; the data interaction module is also used for receiving data analysis results of the data collected by the gnawing monitoring unit and sent from the cloud, and controlling the plurality of rat-repelling components to perform rat-repelling operations.

[0006] Furthermore, the inner protective layer is composed of a polyethylene inner sheath and an aramid yarn layer; the physical rat-proof layer includes a plurality of rat-proof reinforcement tubes evenly arranged around the inner protective layer, wherein the rat-proof reinforcement tubes are made of glass fiber reinforced plastic, and the gnawing monitoring unit is arranged between the rat-proof reinforcement tubes and the outer protective layer; the outer protective layer includes a polyethylene outer sheath.

[0007] The present invention provides a method for producing a rat-proof optical cable, which is used to produce the above-mentioned rat-proof optical cable, comprising: obtaining environmental information of a deployment area of ​​the rat-proof optical cable; determining a gnawing risk value of the deployment area according to the environmental information of the deployment area; determining the thickness of a physical rat-proof layer of an optical cable body of the rat-proof optical cable according to the gnawing risk value of the deployment area; determining a spacing of gnawing monitoring devices according to the gnawing risk value of the deployment area; producing an optical cable body of the rat-proof optical cable according to the thickness of the physical rat-proof layer of the optical cable body and the spacing of gnawing monitoring devices; performing a gnawing simulation on the optical cable body of the rat-proof optical cable to obtain gnawing simulation data; determining a gnawing monitoring parameter according to the gnawing simulation data, wherein the gnawing monitoring parameter is used to instruct a data interaction module to upload data collected by a gnawing monitoring unit, and the gnawing monitoring parameter is also used to instruct a cloud to analyze the data collected by the gnawing monitoring unit.

[0008] Furthermore, the gnawing risk value of the deployment area is determined based on the environmental information of the deployment area, including: determining multiple environmental factors; obtaining environmental information and mouse activity information of multiple reference areas based on the multiple environmental factors; determining multiple influencing environmental factors from the multiple environmental factors based on the environmental information and mouse activity information of the multiple reference areas; determining the gnawing risk value of the deployment area based on the multiple influencing environmental factors and the environmental information of the deployment area.

[0009] Furthermore, according to the gnawing risk value of the deployment area, the thickness of the physical rat-proof layer of the optical cable body of the rat-proof optical cable is determined, including: obtaining first gnawing test data of multiple first test optical cable bodies in multiple test scenarios, wherein the gnawing risk values ​​corresponding to any two test scenarios are different, and the thickness of the physical rat-proof layer of any two first test optical cable bodies are different; according to the first gnawing test data of the multiple test optical cable bodies in multiple test scenarios, a first regression model is established, wherein the independent variable of the first regression model is the gnawing risk value, and the dependent variable of the first regression model is the thickness of the physical rat-proof layer; and according to the gnawing risk value of the deployment area, the thickness of the physical rat-proof layer of the optical cable body of the rat-proof optical cable is determined by the first regression model.

[0010] Furthermore, the spacing of the gnawing monitoring devices is determined according to the gnawing risk value of the deployment area, including: for each test scenario, obtaining second gnawing test data of multiple second test optical cable bodies in the test scenario, wherein a gnawing monitoring device is evenly arranged in each second test optical cable body, the spacing of the gnawing monitoring devices in any two second test optical cable bodies is different, and the thickness of the physical rat-proof layer of each second test optical cable body is determined based on the first regression model; establishing a second regression model according to the second gnawing test data of multiple second test optical cable bodies in each test scenario, wherein the independent variable of the second regression model is the gnawing risk value, and the dependent variable of the second regression model is the spacing of the gnawing monitoring devices; determining the spacing of the gnawing monitoring devices according to the gnawing risk value of the deployment area through the second regression model.

[0011] Furthermore, a gnawing simulation is performed on the optical cable body of the rat-proof optical cable to obtain gnawing simulation data, including: determining simulated gnawing parameters according to the gnawing risk value corresponding to each test scenario, the gnawing risk value of the deployment area and the spacing of the gnawing monitoring devices; and controlling the simulated gnawing device to perform a gnawing simulation on the optical cable body of the rat-proof optical cable through the simulated gnawing parameters to obtain gnawing simulation data.

[0012] Further, determining the gnawing monitoring parameters according to the gnawing simulation data includes: determining the associated gnawing monitoring units of each gnawing monitoring unit according to the gnawing simulation data, wherein the gnawing monitoring parameters at least include the associated gnawing monitoring units of each gnawing monitoring unit.

[0013] Further, the biting monitoring parameter instructs the data interaction module to upload the data collected by the biting monitoring unit, including: the data interaction module determines the abnormal biting monitoring unit according to the data collected by the biting monitoring unit, determines the associated biting monitoring unit of the abnormal biting monitoring unit according to the associated biting monitoring unit of each biting monitoring unit, and uploads the data collected by the abnormal biting monitoring unit and the data collected by the associated biting monitoring unit of the abnormal biting monitoring unit.

[0014] Furthermore, the bite monitoring parameters instruct the cloud to analyze the data collected by the bite monitoring unit, including: the cloud determines the bite location based on the data collected by the abnormal bite monitoring unit and the data collected by the bite monitoring units associated with the abnormal bite monitoring unit.

[0015] Furthermore, the data interaction module receives data analysis results of the data collected by the gnawing monitoring unit and sent from the cloud, and controls multiple mouse repellent components to perform mouse repellent operations, including: determining a target mouse repellent component from multiple mouse repellent components according to the gnawing position determined by the cloud; and controlling the target mouse repellent component to perform mouse repellent operations.

[0016] Compared with the prior art, the rat-proof optical cable and its production method provided by the present invention have at least the following beneficial effects: 1. By setting up a physical anti-rat layer, such as using hard materials such as metal armor, steel belt, glass fiber or nylon sheath, it can effectively prevent rodents such as rats from biting the outer skin or internal optical fiber of the optical cable, thereby improving the anti-bite force of the optical cable. When the bite monitoring unit detects abnormal biting, the data interaction module will upload the data to the cloud for analysis. After the cloud confirms the abnormality, the data interaction module can control the mouse repellent component to perform mouse repelling operations to further protect the optical cable from rodent damage. Multiple bite monitoring devices are evenly distributed between the inner and outer protective layers of the optical cable, and can monitor in real time whether the optical cable is bitten. Once the biting behavior is detected, the bite monitoring unit will immediately collect data and upload it to the cloud. The data interaction module is responsible for efficiently and accurately transmitting the bite monitoring data to the cloud. The cloud uses advanced algorithms to analyze these data, quickly identify abnormal bite monitoring units and their associated units, and thus achieve a preliminary judgment of the bite location. Through the analysis of the bite monitoring data in the cloud, the location of the bite can be roughly determined, which helps maintenance personnel quickly locate and repair damaged optical cables and reduce troubleshooting time. Not only can it repair the optical cable in time after it is damaged, but it can also detect potential gnawing risk areas through long-term data monitoring and analysis, so as to carry out preventive maintenance and reduce the risk of communication interruption caused by optical cable damage.

[0017] 2. By obtaining the environmental information of the area where the rat-proof optical cable is laid, the rat-damage situation, soil conditions, climate conditions, etc. in the area can be accurately understood, so as to determine the gnawing risk value of the laying area based on this information. According to the gnawing risk value, the thickness of the physical rat-proof layer of the cable body of the rat-proof optical cable and the spacing of the gnawing monitoring device can be customized to ensure that the optical cable has the best rat-proof effect in the laying area. Through customized production, the thickness of the physical rat-proof layer and the spacing of the gnawing monitoring device can be adjusted according to actual needs to avoid unnecessary waste of resources. Under the premise of ensuring the rat-proof effect, by optimizing resource allocation, the production cost of the optical cable can be reduced and the economic benefit can be improved. Through gnawing simulation, the performance data of the optical cable under simulated gnawing conditions can be obtained to determine the gnawing monitoring parameters. These parameters can guide the data interaction module to upload the data collected by the gnawing monitoring unit more accurately, and help the cloud to analyze the gnawing monitoring data more accurately. This not only improves the response speed and accuracy of the optical cable when it is bitten, but also enhances the reliability and stability of the optical cable. The customized rat-proof optical cable has clear gnawing monitoring parameters and physical rat-proof layer thickness, which enables maintenance personnel to locate the problem more quickly when the optical cable fails. At the same time, through the analysis of gnawing monitoring data in the cloud, potential fault points can be predicted, so as to carry out preventive maintenance and reduce the occurrence of optical cable failures. This production method integrates advanced technologies such as environmental information acquisition, customized production, gnawing simulation and data analysis, and promotes the innovation and development of optical cable rat-proofing technology. With the continuous advancement of technology and the deepening of application, this production method will be further improved and optimized, providing strong support for the upgrading and development of the optical cable industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 is a schematic structural diagram of an optical cable body according to some embodiments of this specification; Figure 2 It is a schematic flow chart of a method for producing a rat-proof optical cable according to some embodiments of this specification; Figure 3 It is a schematic diagram of a process for determining the thickness of a physical rodent-proof layer and the spacing of a gnawing monitoring device according to some embodiments of this specification.

[0019] In the figure, 110, inner protective layer; 111, polyethylene inner sheath; 112, aramid yarn layer; 120, outer protective layer; 130, optical cable unit; 140, physical rat-proof layer; 141, rat-proof reinforced tube; 150, gnawing monitoring device. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0021] Figure 1 is a schematic diagram of the structure of an optical cable body according to some embodiments of this specification, such as Figure 1 As shown, the optical cable body includes an inner protective layer 110 and an outer protective layer 120, a plurality of optical cable units 130 are arranged in the inner protective layer 110, a physical rat-proof layer 140 is arranged between the inner protective layer 110 and the outer protective layer 120, a gnawing monitoring unit is arranged between the physical rat-proof layer 140 and the inner protective layer 110, and the gnawing monitoring unit includes a plurality of gnawing monitoring devices 150, and the distance between two adjacent gnawing monitoring devices 150 is consistent.

[0022] Specifically, the inner protective layer 110 is composed of a polyethylene inner sheath 111 and an aramid yarn layer 112. The physical rat-proof layer 140 includes a plurality of rat-proof reinforcement tubes 141 uniformly arranged around the inner protective layer, wherein the rat-proof reinforcement tubes 141 are made of glass fiber reinforced plastic, and the gnawing monitoring unit is arranged between the side wall of the rat-proof reinforcement tube 141 facing the outer protective layer 120 and the outer protective layer 120. The outer protective layer 120 includes a polyethylene outer sheath.

[0023] Preferably, the rat-proof reinforced tube 141 is made of glass fiber reinforced plastic with a diameter ≥2.1mm, a tensile Young's modulus ≥50GPa, and a bending Young's modulus ≥45GPa; the material has the characteristics of high hardness, light weight, non-conductivity, and good anti-gnaw effect; the outer protective layer 120 is made of ordinary black polyethylene in lines below 12kV and a high-density black anti-corrosion material in lines greater than 12kV and less than 25kV according to the user's use environment requirements.

[0024] Just as an example, the technical parameters of an optical cable body may be as shown in the technical parameter table of the optical cable body.

[0025] Technical parameter table of optical cable body

[0026] A rat-proof optical cable may also include a data interaction module arranged on the optical cable body, which is used to upload the data collected by the gnawing monitoring unit to the cloud, and the cloud is used to analyze the data collected by the gnawing monitoring unit.

[0027] A rat-proof optical cable may also include a rat-repelling module, including a plurality of rat-repelling components arranged on the optical cable body. As an example only, the rat-repelling component may include a sound rat-repelling device for emitting sound to repel rats.

[0028] The data interaction module is also used to receive data analysis results of the data collected by the gnawing monitoring unit and sent from the cloud, and control multiple mouse repelling components to perform mouse repelling operations.

[0029] Figure 2 is a schematic flow chart of a method for producing a rodent-proof optical cable according to some embodiments of this specification, such as Figure 2 As shown, a method for producing a rodent-proof optical cable may include the following steps.

[0030] Step 210, obtaining environmental information of the area where the rodent-proof optical cable is laid.

[0031] Specifically, the environmental information of the deployment area of ​​the rodent-proof optical cable may include at least the environmental temperature, environmental humidity, environmental light, environmental pressure, human activity intensity, etc. at multiple time points.

[0032] Step 220, determining the gnawing risk value of the deployment area according to the environmental information of the deployment area.

[0033] In some embodiments, step 220 specifically includes: Determine multiple environmental factors (e.g., temperature, humidity, light, pressure, intensity of human activity, etc.); Obtaining environmental information and mouse activity information (e.g., number, etc.) of multiple reference areas based on multiple environmental factors; Determine multiple influencing environmental factors from multiple environmental factors based on environmental information and mouse activity information of multiple reference areas; The gnawing risk value of the deployment area is determined based on multiple influencing environmental factors and environmental information of the deployment area.

[0034] Specifically, by analyzing the temperature and humidity data of each reference area and combining it with the activity habits of mice, it is found that high temperature and high humidity environment may promote the reproduction and activity of mice and increase the risk of damage to optical cables. Therefore, temperature and humidity are important environmental factors. Although light has little direct effect on the performance of optical cable materials, mice that are active at night may be more likely to contact and gnaw optical cables. Therefore, when laying optical cables, the impact of lighting conditions on mouse activities should be considered, and appropriate laying methods and protective measures should be selected. During the laying process, optical cables may be subject to pressure from external environments such as soil and rocks. By analyzing the soil type and topography of each reference area and combining it with the compressive performance requirements of optical cables, the degree of influence of pressure on the anti-rat effect of optical cables can be determined. In areas with soft soil or dense rocks, additional protective measures may be required to prevent damage to the optical cables.

[0035] Clean the collected environmental information of multiple reference areas to remove outliers, missing values ​​or duplicate data to ensure the accuracy and completeness of the data. Since the data of different environmental factors may have different dimensions and value ranges, the data needs to be standardized. Standardization can convert the data into dimensionless values, which is convenient for subsequent calculations and analysis. Common standardization methods include Z-score standardization and Min-Max standardization. For environmental factors that can be quantified, such as temperature, humidity, vegetation coverage, etc., numerical representation can be used directly. For environmental factors that are difficult to quantify directly, such as soil type, vegetation type, human activity type, etc., classification coding can be used for digitization. For example, unique codes or labels can be assigned to different soil types, vegetation types or human activity types. If the environmental factors have spatial distribution characteristics, such as the spatial distribution of soil moisture and vegetation coverage, geographic information system technology can be used for spatial processing to represent the environmental factors as spatial data.

[0036] For example, the correlation coefficient between environmental factors and mouse activity information can be calculated according to the following formula, and environmental factors with correlation coefficients greater than the correlation coefficient threshold are regarded as influencing environmental factors:

[0037]

[0038] in, is the correlation coefficient between the i-th environmental factor and the mouse activity information, , , and is the weight, , , , and are greater than 0, is the maximum correlation coefficient between the i-th environmental factor and the mouse activity information, is the minimum correlation coefficient between the ith environmental factor and the mouse activity information, is the mean correlation coefficient between the i-th environmental factor and the mouse activity information, is the standard deviation correlation coefficient between the i-th environmental factor and the mouse activity information, is the maximum value of the i-th environmental factor in the j-th reference area, is the minimum value of the i-th environmental factor in the j-th reference area, for, is the mean value of the i-th environmental factor in the j-th reference area, is the standard deviation of the i-th environmental factor in the j-th reference area, is the total number of reference areas.

[0039] A bite risk prediction model can be established, wherein the bite risk prediction model can be a convolutional neural network model, and the bite risk prediction model can be used to predict the bite risk value of the deployment area. A feature vector can be extracted from the environmental information of the deployment area according to multiple influencing environmental factors, and the bite risk prediction model can be used to determine the bite risk value of the deployment area according to the feature vector.

[0040] Step 230 , determining the thickness of the physical rat-proof layer 140 of the optical cable body of the rat-proof optical cable according to the gnawing risk value of the deployment area.

[0041] Figure 3 is a schematic diagram of a process for determining the thickness of the physical rodent-proof layer 140 and the spacing between the gnawing monitoring devices according to some embodiments of this specification, such as Figure 3 As shown, in some embodiments, step 230 specifically includes: Obtaining first biting test data of a plurality of first test optical cable bodies in a plurality of test scenarios, wherein the biting risk values ​​corresponding to any two test scenarios are different, and the thickness of the physical rat-proof layer 140 of any two first test optical cable bodies is different. For example, a series of optical cables may be designed, wherein the thickness of the physical rat-proof layer 140 varies from very thin (e.g., 0.5 mm) to very thick (e.g., 5 mm), and the thickness is increased by a certain amount (e.g., 0.5 mm) each time, and at least one of the number of biting times and biting depths in a test cycle corresponding to different biting risk values ​​is different; According to the first biting test data of the multiple test optical cable bodies in multiple test scenarios, a first regression model is established, wherein the independent variable of the first regression model is the biting risk value, and the dependent variable of the first regression model is the thickness of the physical rodent-proof layer 140; The thickness of the physical rat-proof layer 140 of the optical cable body of the rat-proof optical cable is determined according to the gnawing risk value of the deployment area through the first regression model.

[0042] Specifically, a first regression model is constructed based on the collected first biting test data using statistical software or programming tools (such as R, Python, etc.). The first regression model will describe the relationship between the biting risk value and the thickness of the physical rodent-proof layer 140. The accuracy and reliability of the first regression model are ensured by cross-validation or other validation methods.

[0043] The gnawing risk value of the deployment area is input into the first regression model, and the first regression model will output a recommended thickness of the physical rodent-proof layer 140. According to the prediction result of the first regression model, combined with factors such as cost-benefit analysis and cable performance requirements, the final thickness of the physical rodent-proof layer 140 of the cable body of the rodent-proof optical cable is determined.

[0044] Step 240, determining the spacing of the gnawing monitoring devices according to the gnawing risk value of the deployment area.

[0045] like Figure 3 As shown, in some embodiments, step 240 specifically includes: For each test scenario, second gnawing test data of multiple second test optical cable bodies in the test scenario are obtained, wherein a gnawing monitoring device is uniformly arranged in each second test optical cable body, and the spacing between the gnawing monitoring devices in any two second test optical cable bodies is different. The thickness of the physical rodent-proof layer 140 of each second test optical cable body is determined based on the first regression model to ensure that they have corresponding protection capabilities under different gnawing risk values. Second test optical cable bodies equipped with different spacings between gnawing monitoring devices are placed in each test scenario, and a gnawing test is performed to record the gnawing events of each optical cable body at different times, including the gnawing position, gnawing time, and the response of the gnawing monitoring device; According to the second gnawing test data of the plurality of second test optical cable bodies in each test scenario, a second regression model is established, wherein the independent variable of the second regression model is the gnawing risk value, and the dependent variable of the second regression model is the gnawing monitoring device spacing; The spacing between gnawing monitoring devices is determined according to the gnawing risk value of the deployment area through the second regression model.

[0046] Specifically, the second gnawing test data collected is used to construct a second regression model. The second regression model aims to reveal the correlation between the gnawing risk value and the spacing between gnawing monitoring devices. The accuracy of the model is verified by comparing the prediction results of the second regression model with the actual test data. The assessed gnawing risk value is input into the second regression model, and the second regression model will output a recommended spacing between gnawing monitoring devices. Combined with factors such as optical cable cost, maintenance convenience, and monitoring efficiency, the model prediction results are comprehensively considered to determine the final spacing between gnawing monitoring devices.

[0047] Step 250 , producing the main body of the rat-proof optical cable according to the thickness of the physical rat-proof layer 140 of the optical cable main body and the spacing between the gnawing monitoring devices.

[0048] Specifically, the optical cable body of the rat-proof optical cable can be produced through the following process: 1. Prepare the optical cable unit 130 Material selection: Core components of the optical cable unit 130 , such as optical fibers, reinforcing elements (such as steel wires or aramid fibers), and fillers, are carefully selected according to design requirements.

[0049] Manufacturing of the optical cable unit 130: Using precise optical cable manufacturing equipment, assemble the optical fiber, reinforcing elements and fillers according to a predetermined structure to form a stable optical cable unit 130. This step may involve processes such as coloring of the optical fiber, secondary coating and molding of the optical cable unit 130.

[0050] 2. Making the inner protective layer 110 Material selection: Select a material with excellent protective performance for the inner protective layer 110, such as polyethylene or polyvinyl chloride.

[0051] Extrusion molding: Through an extruder, the selected material is evenly wrapped around the outside of the optical cable unit 130 to form the inner protective layer 110. This step is intended to protect the optical cable unit 130 from damage by the external environment.

[0052] 3. Install the bite monitoring unit Preparation of gnawing monitoring devices: Prepare sufficient number of gnawing monitoring devices with consistent spacing according to design requirements. These devices may include vibration sensors, fracture sensors or other intelligent devices that can detect gnawing events.

[0053] Device installation: The gnawing monitoring device is firmly fixed on the optical cable body at a preset spacing between the inner protective layer 110 and the outer protective layer 120. This step needs to ensure that the device is installed in an accurate position and does not affect the overall performance of the optical cable.

[0054] 4. Customized physical rat-proof layer 140 Material selection: A material with a high resistance to chewing is selected for the physical rodent-proof layer 140, such as a special alloy, hard plastic or reinforced polymer.

[0055] Thickness determination: Based on the gnawing risk level of the optical cable, the optimal thickness of the physical rodent-proof layer 140 is determined using the risk assessment model established previously.

[0056] Production of rat-proof layer: The selected material is processed into a physical rat-proof layer 140 that meets the thickness requirements through extrusion, injection molding or other molding processes, and the physical rat-proof layer 140 is tightly wrapped around the outside of the inner protective layer 110 .

[0057] 5. Making the outer protective layer 120 Material selection: Select a material with good wear resistance and weather resistance for the outer protective layer 120, such as high-density polyethylene or low-smoke halogen-free flame retardant material.

[0058] Extrusion molding: Using the extruder again, the selected material is evenly wrapped around the physical rodent-proof layer 140 to form an outer protective layer 120. This step is intended to further enhance the external protection capability of the optical cable.

[0059] 6. Quality inspection and performance testing Appearance inspection: Conduct a comprehensive inspection on the appearance of the optical cable body to ensure that its surface is smooth and undamaged.

[0060] Bite test: In a simulated bite risk environment, a bite test is performed on the optical cable body to verify the performance of the bite monitoring unit and the physical rodent-proof layer 140.

[0061] Other performance tests: According to industry standards or customer requirements, the optical cable body is tested for tensile strength, bending radius, flame retardancy and other performance tests.

[0062] 7. Packaging and storage Optical cable packaging: The optical cable bodies that have passed quality inspection will be packaged according to established specifications to ensure their safety during transportation and storage.

[0063] Labels and records: Paste labels containing information such as cable model, length, production date, etc. on the optical cable packaging, and establish corresponding production records.

[0064] Step 260, performing a gnawing simulation on the optical cable body of the rat-proof optical cable to obtain gnawing simulation data.

[0065] In some embodiments, step 260 specifically includes: Determine the simulated biting parameters (e.g. biting force, speed, and position, etc.) according to the biting risk value corresponding to each test scenario, the biting risk value of the deployment area, and the spacing of the biting monitoring devices; By simulating the gnawing parameters, the simulated gnawing device is controlled to simulate the gnawing of the optical cable body of the rat-proof optical cable to obtain the simulated gnawing data, wherein the simulated gnawing data may include the data collected by the gnawing monitoring unit.

[0066] Specifically, for each test scenario, the absolute value of the difference between the bite risk value of the deployment area and the bite risk value corresponding to the test scenario is calculated, and the test scenario with the smallest absolute value of the difference in bite risk value is used as the target test scenario. The number of bites and the bite depth of a test cycle corresponding to the target test scenario are used as part of the simulated bite parameters. According to the spacing of the bite detection positions, the spacing between the bite positions is determined. The larger the spacing of the bite monitoring devices, the larger the spacing between the bite positions.

[0067] The core components of the simulated gnawing device are the mechanical upper jaw and the mechanical lower jaw, which are connected by an articulated manner and can simulate the upper and lower jaw movements of a mouse. Mechanical teeth are provided at one end of the mechanical upper jaw and the mechanical lower jaw to simulate the teeth of a mouse. The drive unit is responsible for driving the mechanical upper jaw and the mechanical lower jaw to move up and down to simulate the gnawing action of a mouse. It adopts electromagnetic drive, pneumatic drive or hydraulic drive. The sensor is arranged on the mechanical upper jaw and / or the mechanical lower jaw to measure the relevant parameters of the gnawing action, such as the gnawing force, the gnawing speed, etc. The sensor can also be used to monitor the damage of the cable during the gnawing process. The transmission unit includes components such as a limit plate, an upper jaw locator and a lower jaw locator. It is responsible for guiding the movement trajectory of the mechanical upper jaw and the mechanical lower jaw and ensuring the stability and accuracy of the gnawing action. The controller is connected to the sensor and the drive unit to set the gnawing parameters (such as the gnawing force, speed, frequency, etc.) and control the execution of the gnawing action.

[0068] Step 270, determining the biting monitoring parameters according to the biting simulation data, wherein the biting monitoring parameters are used to instruct the data interaction module to upload the data collected by the biting monitoring unit, and the biting monitoring parameters are also used to instruct the cloud to analyze the data collected by the biting monitoring unit.

[0069] In some embodiments, determining the biting monitoring parameters according to the biting simulation data includes: According to the gnawing simulation data, the associated gnawing monitoring unit of each gnawing monitoring unit is determined, wherein the gnawing monitoring parameters at least include the associated gnawing monitoring unit of each gnawing monitoring unit.

[0070] Specifically, the cable body of the rat-proof optical cable is simulated by a simulated gnawing device, and various data during the gnawing process are collected. These data may include gnawing position, gnawing depth, gnawing time, gnawing force, etc. Next, these data are analyzed to understand the characteristics and laws of the gnawing event. Associated gnawing monitoring units refer to adjacent or close gnawing monitoring devices in a gnawing event, which may detect gnawing signals simultaneously or successively. The association relationship between these units helps to more accurately locate the gnawing event and evaluate the degree of gnawing. Analyze the gnawing position data to determine which gnawing monitoring units are adjacent or close in physical position. These units may be triggered at the same time in a gnawing event. Analyze the gnawing time data to determine which gnawing monitoring units have similar detection times in the gnawing event. This helps to identify continuous or simultaneous gnawing events. Analyze the gnawing force data to determine which gnawing monitoring units have similar gnawing forces detected or have some correlation. This helps to evaluate the severity of the gnawing event.

[0071] For example, the biting monitoring correlation coefficient of two biting monitoring units can be calculated based on the following formula, and two biting monitoring units whose biting monitoring correlation coefficient is greater than a biting monitoring correlation coefficient threshold are mutually correlated biting monitoring units:

[0072] in, is the bite monitoring correlation coefficient between the i-th bite monitoring unit and the j-th bite monitoring unit, is the total number of sampled bite simulation time points, is the output value of the vibration sensor of the i-th biting monitoring unit at the t-th sampling biting simulation time point, is the output value of the vibration sensor of the jth gnawing monitoring unit at the tth sampled gnawing simulation time point.

[0073] In some embodiments, the biting monitoring parameter instructs the data interaction module to upload the data collected by the biting monitoring unit, including: The data interaction module determines the abnormal biting monitoring unit based on the data collected by the biting monitoring unit, determines the associated biting monitoring unit of the abnormal biting monitoring unit based on the associated biting monitoring units of each biting monitoring unit, and uploads the data collected by the abnormal biting monitoring unit and the data collected by the associated biting monitoring units of the abnormal biting monitoring unit.

[0074] Specifically, the data interaction module regularly collects data from each biting monitoring unit. The data includes key information such as biting time, location, and strength. The collected data is cleaned to remove invalid or abnormal data. The data is formatted into a unified format for subsequent analysis.

[0075] For each gnawing monitoring unit, the data interaction module may first correct the vibration data collected by the gnawing monitoring unit according to the associated gnawing monitoring unit, and determine the abnormal gnawing monitoring unit according to the corrected vibration data.

[0076] For example, the biting monitoring data collected by the biting monitoring unit can be corrected according to the following process: S11. For each bite monitoring unit, perform variational mode decomposition on the vibration data collected by the bite monitoring unit to obtain the intrinsic mode function and residual corresponding to the vibration data, and perform local mean decomposition on the vibration data collected by the bite monitoring unit to obtain the single component modulation function and residual component of the vibration data; S12. For each biting monitoring unit, the intrinsic mode function and residual of the biting monitoring unit are corrected by a first correction model based on the correlation coefficient between the biting monitoring unit and the associated biting monitoring unit and the intrinsic mode function and residual of each associated biting monitoring unit to obtain the corrected intrinsic mode function and residual of the biting monitoring unit. The single component modulation function and residual component of the biting monitoring unit are corrected by a second correction model based on the correlation coefficient between the biting monitoring unit and the associated biting monitoring unit and the single component modulation function and residual component of each associated biting monitoring unit to obtain the corrected single component modulation function and residual component of the biting monitoring unit, wherein the first correction model and the second correction model may both be convolutional neural networks (CNN). S13, calculating a global correction change parameter based on the corrected intrinsic mode function and residual of each biting monitoring unit and the corrected single component modulation function and residual component; S14, determining whether the global correction change parameter is greater than the global correction change parameter threshold, if so, executing S15, if not, executing S16; S15. For each biting monitoring unit, the corrected intrinsic mode function and residual of the biting monitoring unit are corrected again by using the first correction model based on the correlation coefficient between the biting monitoring unit and the associated biting monitoring unit and the corrected intrinsic mode function and residual of each associated biting monitoring unit, and the corrected intrinsic mode function and residual of the biting monitoring unit are updated. The corrected single component modulation function and residual component of the biting monitoring unit are corrected again by using the second correction model based on the correlation coefficient between the biting monitoring unit and the associated biting monitoring unit and the corrected single component modulation function and residual component of each associated biting monitoring unit to obtain the corrected single component modulation function and residual component of the biting monitoring unit, and execute S13. S16. For each gnawing monitoring unit, determine the corrected vibration data of the gnawing monitoring unit based on the corrected intrinsic mode function and residual of the gnawing monitoring unit and the corrected single component modulation function and residual component through a third correction model, wherein the third correction model can be a Generative Adversarial Networks (GAN) model.

[0077] Specifically, for each bite monitoring unit, the similarity between the t-th corrected intrinsic mode function and residual and the t+1-th corrected intrinsic mode function and residual can be calculated, the similarity between the t-th corrected single component modulation function and residual component and the t+1-th corrected single component modulation function and residual component can be calculated, and the second corrected change parameter of the bite monitoring unit can be calculated. Specifically, the first similarity determination model can be based on the time domain characteristics and frequency domain characteristics of the t-th corrected intrinsic mode function and the time domain characteristics and frequency domain characteristics of the residual, and the first cosine similarity between the t-th corrected intrinsic mode function and residual and the t+1-th corrected intrinsic mode function and residual can be calculated. The second similarity determination model is used to calculate the second cosine similarity between the single component modulation function and the residual component after the tth correction and the single component modulation function and the residual component after the t+1th correction based on the time domain characteristics and frequency domain characteristics of the single component modulation function after the tth correction and the time domain characteristics and frequency domain characteristics of the residual component and the time domain characteristics and frequency domain characteristics of the single component modulation function after the t+1th correction. The correction change parameter of the bite monitoring unit after the t+1th correction is calculated based on the first similarity and the second similarity according to the following formula:

[0078] in, is the modified change parameter of the ith biting monitoring unit after the t+1th modification, and is the preset weight, and and are greater than 0, and , and is the preset parameter, and and Greater than 0, is the first cosine similarity of the i-th biting monitoring unit, is the second cosine similarity of the i-th biting monitoring unit.

[0079] The weighted sum of the corrected change parameters of each gnawing monitoring unit after the t+1th correction is performed to calculate the global corrected change parameter after the t+1th correction.

[0080] For each gnawing monitoring unit, the data interaction module can perform variational modal decomposition on the corrected vibration data corresponding to the gnawing monitoring unit to obtain the intrinsic mode function and residual corresponding to the corrected vibration data, and perform local mean decomposition on the corrected vibration data corresponding to the gnawing monitoring unit to obtain the single component modulation function and residual component corresponding to the corrected vibration data. In addition, the data interaction module can perform variational modal decomposition on the corrected vibration data corresponding to the associated gnawing monitoring unit of the gnawing monitoring unit to obtain the intrinsic mode function and residual corresponding to the corrected vibration data, and perform local mean decomposition on the corrected vibration data corresponding to the associated gnawing monitoring unit of the gnawing monitoring unit to obtain the single component modulation function and residual component corresponding to the corrected vibration data. Whether the gnawing monitoring unit is an abnormal gnawing monitoring unit is determined by an abnormality determination model, wherein the input of the abnormality determination model includes at least the intrinsic mode function and residual corresponding to the corrected vibration data of the gnawing monitoring unit, the intrinsic mode function and residual and the intrinsic mode function and residual corresponding to the corrected vibration data of the associated gnawing monitoring unit, and the abnormality determination model can be a convolutional neural network model.

[0081] When the data interaction module determines that the biting monitoring unit D is an abnormal unit, it will send an alarm signal to the central processor. After receiving the alarm signal, the central processor can trigger a series of subsequent processing measures, such as: Send a notification to maintenance personnel to inform them of the presence of abnormal gnawing monitoring units and their specific locations.

[0082] Record abnormal events for subsequent analysis and improvement.

[0083] If the situation is serious, the power supply to the optical cable can be automatically cut off or other emergency measures can be taken to prevent further damage.

[0084] In some embodiments, the bite monitoring parameter instructs the cloud to analyze the data collected by the bite monitoring unit, including: The cloud determines the biting location based on the data collected by the abnormal biting monitoring unit and the data collected by the biting monitoring units associated with the abnormal biting monitoring unit.

[0085] As an example only, after receiving abnormal data, the cloud performs the following analysis: Abnormal data analysis: The data from the biting monitoring unit D is abnormal, and the biting force is obviously too strong.

[0086] The biting intensity of the associated biting monitoring units B and C adjacent to D is also relatively high, but does not reach abnormal values.

[0087] The biting force of monitoring units A and E far away from D is relatively low.

[0088] Position Inference: According to the magnitude and distribution of the biting force, it can be inferred that the biting incident is likely to have occurred in the biting monitoring unit D and its surrounding areas.

[0089] Since the biting force of B and C was also relatively high, it further confirmed the inference that the biting incident occurred in this area.

[0090] Precise location determination: If each bite monitoring unit is equipped with precise location information (such as GPS coordinates or relative position markers on the optical cable), the exact location of the bite can be determined directly.

[0091] In this example, assuming that each monitoring unit is marked with its relative position on the cable, it can be inferred that the bite occurred at a point between unit D and its adjacent area (i.e., between units B and C).

[0092] Comprehensive consideration: In addition to biting force, other factors such as biting duration, biting frequency, etc. can also be considered to more fully assess the severity and location of the biting incident.

[0093] In this case, since the biting events occurred continuously within a short period of time and the intensity gradually increased, it further supports the inference that the biting occurred in unit D and its surrounding areas.

[0094] In some embodiments, the data interaction module receives the data analysis results of the data collected by the biting monitoring unit from the cloud, and controls multiple mouse repelling components to perform mouse repelling operations, including: According to the biting position determined by the cloud, a target mouse repellent component is determined from multiple mouse repellent components; Control the target mouse repelling component to perform mouse repelling operation.

[0095] Specifically, the target mouse repelling component may be the target mouse repelling component that is closest to the biting position.

[0096] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A rat-proof optical cable, characterized in that: include: The optical cable body comprises an inner protective layer and an outer protective layer, a plurality of optical cable units are arranged in the inner protective layer, a physical rat-proof layer is arranged between the inner protective layer and the outer protective layer, a gnawing monitoring unit is arranged between the physical rat-proof layer and the inner protective layer, the gnawing monitoring unit comprises a plurality of gnawing monitoring devices, and the distances between two adjacent gnawing monitoring devices are consistent; A data interaction module provided on the optical cable body, used for uploading the data collected by the gnawing monitoring unit to the cloud, and the cloud is used for analyzing the data collected by the gnawing monitoring unit; A rat repelling module, comprising a plurality of rat repelling components arranged on the optical cable body; The data interaction module is also used to receive data analysis results of the data collected by the gnawing monitoring unit and sent from the cloud, and control multiple mouse driving components to perform mouse driving operations.

2. The rat-proof optical cable according to claim 1, characterized in that: The inner protective layer is composed of a polyethylene inner sheath and an aramid yarn layer; The physical rat-proof layer comprises a plurality of rat-proof reinforcement tubes uniformly arranged around the inner protective layer, wherein the rat-proof reinforcement tubes are made of glass fiber reinforced plastic, and the gnawing monitoring unit is arranged between the rat-proof reinforcement tubes and the outer protective layer; The outer protective layer includes a polyethylene outer sheath.

3. A method for producing a rat-proof optical cable, characterized in that: Used to produce the rat-proof optical cable according to claim 1 or 2, comprising: Obtain environmental information of the area where the rodent-proof optical cable is laid; Determine the gnawing risk value of the deployment area according to the environmental information of the deployment area; Determine the thickness of the physical rat-proof layer of the optical cable body of the rat-proof optical cable according to the gnawing risk value of the deployment area; Determine the spacing of gnawing monitoring devices according to the gnawing risk value of the deployment area; According to the thickness of the physical rodent-proof layer of the cable body and the spacing of the gnawing monitoring devices, the cable body of the rodent-proof cable is produced; Conduct a gnawing simulation on the main body of the rat-proof optical cable to obtain gnawing simulation data; Determine a gnawing monitoring parameter according to the gnawing simulation data, wherein the gnawing monitoring parameter is used to instruct the data interaction module to upload the data collected by the gnawing monitoring unit, and the gnawing monitoring parameter is also used to instruct the cloud to analyze the data collected by the gnawing monitoring unit.

4. The method for producing a rodent-proof optical cable according to claim 3, characterized in that: According to the environmental information of the deployment area, determine the gnawing risk value of the deployment area, including: Identify multiple environmental factors; Obtain environmental information and mouse activity information of multiple reference areas based on multiple environmental factors; Determining a plurality of influencing environmental factors from the plurality of environmental factors according to the environmental information of the plurality of reference areas and the mouse activity information; A gnawing risk value of the deployment area is determined according to the multiple influencing environmental factors and environmental information of the deployment area.

5. The method for producing a rodent-proof optical cable according to claim 4, characterized in that: According to the gnawing risk value of the deployment area, the thickness of the physical rat-proof layer of the cable body of the rat-proof optical cable is determined, including: Acquire first biting test data of a plurality of first test optical cable bodies in a plurality of test scenarios, wherein the biting risk values ​​corresponding to any two test scenarios are different, and the thicknesses of the physical rat-proof layers of any two first test optical cable bodies are different; Establishing a first regression model according to first biting test data of multiple test optical cable bodies in multiple test scenarios, wherein the independent variable of the first regression model is the biting risk value, and the dependent variable of the first regression model is the thickness of the physical rodent-proof layer; Determine the thickness of the physical rat-proof layer of the optical cable body of the rat-proof optical cable according to the gnawing risk value of the deployment area through the first regression model; Determine the spacing of the gnawing monitoring devices based on the gnawing risk value of the deployment area, including: For each test scenario, second gnawing test data of multiple second test optical cable bodies in the test scenario are obtained, wherein a gnawing monitoring device is evenly distributed in each second test optical cable body, the spacing between the gnawing monitoring devices in any two second test optical cable bodies is different, and the thickness of the physical rodent-proof layer of each second test optical cable body is determined based on the first regression model; Establishing a second regression model according to the second gnawing test data of the plurality of second test optical cable bodies in each test scenario, wherein the independent variable of the second regression model is the gnawing risk value, and the dependent variable of the second regression model is the gnawing monitoring device spacing; The spacing between gnawing monitoring devices is determined according to the gnawing risk value of the deployment area through the second regression model.

6. A method for producing a rodent-proof optical cable according to claim 4 or 5, characterized in that: Conduct a gnawing simulation on the main body of the rat-proof optical cable to obtain gnawing simulation data, including: Determine the simulated biting parameters according to the biting risk value corresponding to each test scenario, the biting risk value of the deployment area, and the spacing between biting monitoring devices; By simulating the gnawing parameters, the simulated gnawing device is controlled to simulate the gnawing of the optical cable body of the rat-proof optical cable to obtain the gnawing simulation data.

7. A method for producing a rodent-proof optical cable according to any one of claims 2 to 5, characterized in that: Determine the biting monitoring parameters according to the biting simulation data, including: According to the gnawing simulation data, an associated gnawing monitoring unit of each gnawing monitoring unit is determined, wherein the gnawing monitoring parameters at least include the associated gnawing monitoring unit of each gnawing monitoring unit.

8. The method for producing a rodent-proof optical cable according to claim 7, characterized in that: The biting monitoring parameter instructs the data interaction module to upload the data collected by the biting monitoring unit, including: The data interaction module determines the abnormal biting monitoring unit based on the data collected by the biting monitoring unit, determines the associated biting monitoring unit of the abnormal biting monitoring unit based on the associated biting monitoring units of each biting monitoring unit, and uploads the data collected by the abnormal biting monitoring unit and the data collected by the associated biting monitoring units of the abnormal biting monitoring unit.

9. The method for producing a rodent-proof optical cable according to claim 7, characterized in that: The biting monitoring parameter instructs the cloud to analyze the data collected by the biting monitoring unit, including: The cloud determines the biting location based on the data collected by the abnormal biting monitoring unit and the data collected by the biting monitoring units associated with the abnormal biting monitoring unit.

10. The method for producing a rodent-proof optical cable according to claim 9, characterized in that: The data interaction module receives the data analysis results of the data collected by the biting monitoring unit and controls the multiple mouse-repelling components to perform mouse-repelling operations, including: According to the biting position determined by the cloud, a target mouse repellent component is determined from multiple mouse repellent components; Control the target mouse repellent component to perform mouse repellent operation.

Images