Track running device based on energy and power saving

Abstract

The invention relates to the technical field of intelligent manufacturing equipment industry, in particular to an energy-saving and power-saving track running device which comprises a collection node group, an energy recovery device, an intelligent module, a conveying belt, a permanent magnet synchronous generator and energy storage equipment. The intelligent module comprises a data acquisition unit, an energy recovery unit, an energy release unit, a change prediction unit and a fault adjustment unit, the data acquisition unit acquires track operation data and device operation data, the energy recovery unit adjusts a recovery mode, and the energy release unit uses recovered energy for device operation. The change prediction unit optimizes the recovery frequency, the fault adjustment unit supplements missing data, and the device improves the energy utilization rate, reduces the energy consumption, enhances the system stability and the fault response capability through multi-unit cooperation, and is suitable for modern industrial production scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing equipment industry, and particularly relates to an orbital operation device based on energy saving and power saving. Background Art

[0002] At present, the energy recovery methods of traditional orbital operation energy recovery devices are single and lack coordination, and effective coordination cannot be achieved among various recovery methods, resulting in limited energy recovery efficiency and inability to fully utilize various energy sources during the operation of the device. During the operation of the orbital operation device, various sensors are relied on to collect data to achieve functions such as intelligent control, fault monitoring, and energy management. However, due to the complex orbital operation environment, data transmission between sensors and the control system often interrupts. The interruption of data continuity will cause deviation in intelligent control, affecting transportation safety and efficiency. For the energy recovery system, discontinuous data will interfere with the coordinated control of multiple energy recovery methods by the energy management module, reducing the energy recovery efficiency.

[0003] Chinese Patent Publication No.: CN1125180A discloses an energy-saving device for automobiles and rail vehicles, which consists of a self-provided transmission power circuit and an engine assistance circuit. However, this solution still has the problems of single energy recovery method and lack of coordination with the energy release method, inability to fully utilize various energy sources during the operation of the device, and limited energy recovery efficiency due to data loss. Summary of the Invention

[0004] Therefore, the present invention provides an orbital operation device based on energy saving and power saving to overcome the problems in the prior art that the energy recovery method is single and lacks coordination with the energy release method, resulting in inability to fully utilize various energy sources during the operation of the device, and limited energy recovery efficiency due to data loss.

[0005] To achieve the above object, the present invention provides an orbital operation device based on energy saving and power saving, including:

[0006] A collection node group, which is connected to the conveyor belt and is used for collecting orbital operation data and device operation data;

[0007] An energy recovery device, which is connected to the intelligent module and the conveyor belt and is used for recovering and storing target energy;

[0008] An intelligent module, which is connected to the energy recovery device and the conveyor belt and is used for performing intelligent control on the device. The intelligent control refers to the process of controlling the device according to the orbital operation data and the device operation data;

[0009] A conveyor belt, which is connected to the collection node group, the energy recovery device, the intelligent module, and the permanent magnet synchronous generator and is used for conveying goods;

[0010] A permanent magnet synchronous generator, which is connected to a conveyor belt and an energy storage device, and is used for converting kinetic energy into first target electric energy and potential energy into third target electric energy;

[0011] An energy storage device, which is connected to the permanent magnet synchronous generator and is used for supplying power to the device.

[0012] Furthermore, the intelligent module includes:

[0013] A data acquisition unit, which is used for acquiring track operation data and device operation data;

[0014] An energy recovery unit, which is used for recovering target electric energy according to the track operation data through an energy recovery mode, and is also used for coordinating the energy recovery mode according to an energy coordination method to obtain an energy recovery result;

[0015] An energy release unit, which is used for dividing the demands of the track load according to the track operation data to obtain a demand division level, and formulating an energy release control rule according to the energy recovery result through an energy release control rule formulation method;

[0016] A change prediction unit, which is used for constructing an energy change prediction model according to an energy change prediction model construction method, and optimizing the energy coordination method according to the energy change prediction model;

[0017] A fault adjustment unit, which constructs a continuous data prediction model according to a continuous data prediction model construction method, and supplements the missing data in the track operation data according to the continuous data prediction model.

[0018] Furthermore, the energy recovery unit recovers target electric energy through an energy recovery mode. The energy recovery mode includes a kinetic energy recovery mode, a thermal energy recovery mode, and a potential energy recovery mode. The target electric energy includes first target electric energy, second target electric energy, and third target electric energy. The first target electric energy is recovered through the kinetic energy recovery mode, the second target electric energy is recovered through the thermal energy recovery mode, and the third target electric energy is recovered through the potential energy recovery mode;

[0019] When the energy recovery unit recovers the first target electric energy, it recovers the first target electric energy through the kinetic energy recovery mode.

[0020] Furthermore, the energy recovery unit obtains an energy-saving requirement index A, compares the energy-saving requirement index A with a preset requirement index A0, judges the high or low situation of the energy-saving requirement index according to the comparison result, and adjusts the target control weight k1 according to the judgment result.

[0021] Further, when the energy recovery unit recovers the second target electric energy, it recovers the second target electric energy through a heat energy recovery mode, and the heat energy recovery mode includes: a thermoelectric material converts the temperature difference generated by the conveyor belt into thermoelectric energy according to the Seebeck effect, takes the thermoelectric energy as the second target electric energy, and recovers the second target electric energy to the energy recovery device;

[0022] When the energy recovery unit recovers the third target electric energy, it recovers the third target electric energy through a potential energy recovery mode, and the potential energy recovery mode includes: the potential energy possessed by the goods during transportation on the conveyor belt drives a permanent magnet synchronous generator to form an induced electromotive force and an induced current, obtains the third target electric energy, and recovers the third target electric energy to the energy recovery device.

[0023] Further, the energy recovery unit coordinates the energy recovery mode according to an energy coordination method to obtain an energy recovery result.

[0024] Further, the energy release unit compares the track load P` with each preset track load power, and the preset track load powers include a first preset track load power P`1 and a second preset track load P`2. It is set that P`1 = 30 kW and P`2 = 80 kW, and judges and divides the level of the demand for the track load P` according to the comparison result, and outputs the divided level of the demand for the track load P` according to the judgment result;

[0025] The energy release unit formulates an energy release control rule according to the demand division level through an energy release control rule formulation method;

[0026] The energy release unit obtains the current device operating temperature through a temperature sensor, compares the current device operating temperature W with a preset temperature W0, judges the state of the current device operating temperature according to the comparison result, and adjusts the track load P` according to the judgment result.

[0027] Further, the change prediction unit constructs an energy change prediction model according to an energy change prediction model construction method.

[0028] Further, when the change prediction unit adjusts the energy coordination method according to the energy change prediction model, the process is as follows:

[0029] Take the track load power, DC bus voltage, device voltage, device current, and current device operating temperature in the track operation data as model input data, input the model input data into the energy change prediction model, and the energy change prediction model outputs an energy change trend result, and the energy change trend result includes a load lightening trend and a load increasing trend, and optimizes the recovery frequency k2 according to the energy change trend result.

[0030] Furthermore, the fault adjustment unit inputs the track load power, DC bus voltage, device voltage, device current, and current device operating temperature into a continuous data prediction model, outputs the corresponding parameter data within a preset prediction coverage duration Ty output by the continuous data prediction model as the continuous data prediction model result, and uses the corresponding parameter data within the preset prediction coverage duration Ty as supplementary data to supplement the missing data of the track operation data;

[0031] The fault adjustment unit obtains the ratio of the number of faulty devices to the number of normally operating devices, uses it as the fault ratio H, compares the fault ratio H with a preset fault ratio H0, judges the fault ratio situation according to the comparison result, and adjusts the preset prediction coverage duration Ty according to the judgment result.

[0032] Compared with the prior art, the beneficial effect of the present invention is that the energy recovery unit collects target energy in multiple dimensions according to various energy recovery methods, coordinates the energy recovery unit and the energy release unit, makes full use of various energy sources during the operation of the device, and at the same time supplements the missing data in the track operation data according to the continuous data prediction model to improve the energy recovery efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a schematic structural diagram of the track operation device based on energy conservation and power saving in this embodiment;

[0034] Figure 2 is a schematic structural diagram of the intelligent module in this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In order to make the purpose and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

[0037] Please refer to Figure 1 as shown, which is a schematic structural diagram of the track operation device based on energy conservation and power saving in this embodiment. The device includes:

[0038] The acquisition node group 1, which is connected to the conveyor belt 4 and is used to acquire track operation data and device operation data;

[0039] The energy recovery device 2, which is connected to the intelligent module 3 and the conveyor belt 4 and is used to recover and store the target energy;

[0040] An intelligent module 3, which is connected to the energy recovery device 2 and the conveyor belt 4, is used to control the device intelligently. The intelligent control refers to the process of controlling the device according to the track operation data and the device operation data;

[0041] A conveyor belt 4, which is connected to the acquisition node group 1, the energy recovery device 2, the intelligent module 3, and the permanent magnet synchronous generator 5, is used to convey goods;

[0042] A permanent magnet synchronous generator 5, which is connected to the conveyor belt 4 and the energy storage device 6, is used to convert kinetic energy into first target electric energy and potential energy into third target electric energy;

[0043] An energy storage device 6, which is connected to the permanent magnet synchronous generator 5, is used to supply power to the device.

[0044] Specifically, the device is applied to modern industrial production scenarios, such as the steel and automobile manufacturing industries. The device collects the track operation data and the device operation data through the acquisition node group 1 and transmits them to the intelligent module 3, and conducts intelligent control on the device according to the track operation data and the device operation data to improve the power saving efficiency of the device. The device recovers the target energy through the energy recovery device 2 to reduce energy waste and improve energy utilization efficiency. The device connects each component through the conveyor belt 4 to realize the conveyance of goods, and cooperates with the permanent magnet synchronous generator 5 to convert kinetic energy and potential energy into electric energy to supply power to the energy storage device 6, so as to reduce the energy consumption of the device, thereby reducing costs and increasing efficiency, saving energy, and improving the effect of energy conservation and power saving.

[0045] Specifically, the acquisition node group 1 collects the track operation data by integrating a rotational speed sensor, a pressure sensor, a voltage sensor, a current sensor, a torque sensor, a temperature sensor, and an electromagnetic sensor, and collects the device operation data through system automatic recording. The track operation data includes the rotational speed of the permanent magnet synchronous generator, the track load power, the DC bus voltage, the device voltage, the device current, the torque of the permanent magnet synchronous generator, the current device working temperature, and the electromagnetic intensity. The rotational speed of the permanent magnet synchronous generator is collected by the rotational speed sensor, the track load power is collected by the pressure sensor, the DC bus voltage and the device voltage are collected by the voltage sensor, the device current is collected by the current sensor, the torque of the permanent magnet synchronous generator is collected by the torque sensor, the current device working temperature is collected by the temperature sensor, and the electromagnetic intensity is collected by the electromagnetic sensor. The device operation data includes the energy saving requirement index, the track transportation time, the number of faulty devices, and the number of normally working devices.

[0046] Specifically, the track operation data refers to a dataset of various types of information that reflects the operation status of the device during its operation, including the rotational speed of the permanent magnet synchronous generator, the track load, the DC bus voltage, the device voltage, the device current, the torque of the permanent magnet synchronous generator, the current operating temperature of the device, and the electromagnetic intensity. The device operation data refers to a dataset that reflects the information on the working status of the device itself, including the energy-saving requirement index, the track transportation time, the number of faulty devices, and the number of normally operating devices. The system automatic recording refers to a system that can obtain and retain various key information in real time and continuously without manual intervention. The device refers to a track operation device based on energy saving. The rotational speed of the permanent magnet synchronous generator refers to the number of revolutions of the rotor of the permanent magnet synchronous generator per unit time, which is obtained by collecting the rotational speed of the permanent magnet synchronous generator through a speed sensor. The unit time refers to a preset time length. In this embodiment, the unit time is set to 1 min. The track load power refers to the energy consumed by the load in the device per unit time. The DC bus voltage refers to the voltage value on the common DC link, which is obtained by a voltage sensor. The device voltage refers to the voltage difference across the device when it is operating normally. The device current refers to the amount of charge passing through the cross-section of its conductor per unit time during the normal operation of the device. The torque of the permanent magnet synchronous generator refers to the measure of the force that causes the rotor of the permanent magnet synchronous generator to rotate. The current operating temperature of the device refers to the temperature of the device that is currently running at the current moment. The electromagnetic intensity refers to the electric field intensity and the magnetic induction intensity. The electric field intensity refers to the physical quantity that represents the strength and direction of the electric field. The magnetic induction intensity refers to the physical quantity that describes the strength and direction of the magnetic field. The energy-saving requirement index refers to a quantitative index that measures the expected energy-saving degree of the device. The track transportation time refers to the time interval in the device from the start of timing when the goods enter the track transportation starting point to the arrival of the goods at the predetermined end position. The number of faulty devices refers to the number of devices that cannot operate normally in the device. The number of normally operating devices refers to the number of devices that can operate normally in the device.

[0047] Specifically, the collection node group 1 integrates multiple types of sensors to comprehensively collect track operation data, improving the intelligence and safety of the system.

[0048] Specifically, the energy recovery device 2 recovers the target energy, and the target energy includes the first target electric energy, the second target electric energy, and the third target electric energy.

[0049] Specifically, the target energy refers to the set of energies captured by the energy recovery device 2 and converted into electrical energy during the operation of the device. The first target electrical energy refers to the electrical energy converted from the kinetic energy generated during the operation of the device. The second target electrical energy refers to the electrical energy converted from the thermal energy generated during the operation of the device. The third target electrical energy refers to the electrical energy converted from the potential energy generated during the operation of the device.

[0050] Specifically, the energy recovery device 2 converts the kinetic energy, thermal energy, and potential energy generated during the operation of the device into electrical energy, improving energy utilization efficiency, saving energy and reducing emissions, and reducing costs and increasing efficiency.

[0051] Specifically, the conveyor belt 4 is made of thermoelectric materials and is used to convey goods.

[0052] Specifically, the thermoelectric material refers to a functional material that can convert thermal energy and electrical energy into each other. The goods refer to the finished industrial materials and semi-finished industrial materials that need to be transported in industrial production.

[0053] Specifically, when the conveyor belt 4 conveys goods, it converts thermal energy into electrical energy, realizes energy recovery, and improves the power saving efficiency of the device;

[0054] Specifically, the permanent magnet synchronous generator 5 includes the excitation system and phase control system of the permanent magnet synchronous generator.

[0055] Specifically, the excitation system of the permanent magnet synchronous generator refers to a device that indirectly achieves a similar excitation effect to a traditional generator by regulating the output current of the inverter. The phase control system refers to a device used to precisely adjust the phase of the output current or voltage of the permanent magnet synchronous generator.

[0056] Specifically, the permanent magnet synchronous generator 5 coordinates through the excitation system and phase control system of the permanent magnet synchronous generator to achieve efficient energy conversion and ensure stable power generation.

[0057] Specifically, the energy storage device 6 uses an energy storage device to supply power to the device, and the energy storage device 6 records the remaining power value of the energy storage device.

[0058] Specifically, in this embodiment, the energy storage device is not limited. In this embodiment, the energy storage device is not limited. Those skilled in the art can set it according to actual needs, as long as it can supply power to the device of the present invention. For example, a lithium-ion battery can be used as the energy storage device.

[0059] Specifically, the energy storage device 6 stores excess electrical energy, ensures stable power supply for the device, records the remaining power for reasonable allocation, and improves energy utilization efficiency.

[0060] Please refer to Figure 2As shown, it is a schematic structural diagram of the intelligent module in this embodiment. The intelligent module includes:

[0061] A data acquisition unit for acquiring track operation data and device operation data;

[0062] An energy recovery unit for recovering target electric energy according to the track operation data through an energy recovery mode, and also for coordinating the energy recovery mode according to an energy coordination method to obtain an energy recovery result. The energy recovery unit is connected to the data acquisition unit;

[0063] An energy release unit for dividing the demands of the track load according to the track operation data to obtain a demand division level, and for formulating an energy release control rule according to the energy recovery result through an energy release control rule formulation method. The energy release unit is connected to the energy recovery unit;

[0064] A change prediction unit for constructing an energy change prediction model according to an energy change prediction model construction method, and for optimizing the energy coordination method according to the energy change prediction model. The change prediction unit is connected to the energy recovery unit;

[0065] A fault adjustment unit for constructing a continuous data prediction model according to a continuous data prediction model construction method, and for supplementing the missing data in the track operation data according to the continuous data prediction model. The energy recovery unit is connected to the data acquisition unit.

[0066] Specifically, the intelligent module is applied to the track operation device based on energy conservation and power saving. The intelligent module operates through multi-unit collaboration to collect target energy in multiple dimensions, collaborate on energy recovery and release, and fault adjustment, so as to improve the energy recovery efficiency of the device. Among them, the intelligent module collects various types of operation data through the data acquisition unit to provide a data basis for the device, so as to improve the intelligent level. The intelligent module uses a fuzzy control algorithm to recover kinetic energy through the energy recovery unit, recovers thermal energy using the Seebeck effect, generates electricity by recovering gravitational potential energy, and coordinates the energy recovery mode to improve the overall energy-saving effect. The intelligent module divides the track load requirements through the energy release unit, formulates energy release control rules, adjusts relevant parameters in combination with the device state, and accurately matches the energy distribution to ensure the stable operation of the device. The intelligent module constructs a prediction model through a long short-term memory network to predict energy changes, optimize the recovery and release power, and adjust strategies in advance to adapt to the energy requirements of different scenarios. The intelligent module constructs a recurrent neural network model through the change prediction unit to supplement missing data, so as to reduce the impact of faults and improve the reliability of the device. The intelligent module supplements the missing data in the track operation data through the fault adjustment unit to improve the operation accuracy of the device, thereby realizing intelligent control, reducing energy consumption, reducing faults, saving electricity, and improving the sustainability of the device.

[0067] Specifically, the data acquisition unit acquires track operation data and device operation data. Among them, the data acquisition unit acquires track operation data and device operation data through a collection node group. The track operation data includes the rotational speed of the permanent magnet synchronous generator, track load, DC bus voltage, device voltage, device current, torque of the permanent magnet synchronous generator, current device operating temperature, and electromagnetic intensity. The data acquisition unit acquires device operation data through system automatic recording. The device operation data includes the energy-saving requirement index, track transportation time, number of faulty devices, and number of normally operating devices.

[0068] Specifically, the data acquisition unit collects various types of operation data through a collection node group to provide a data basis for the device, so as to improve the intelligent level.

[0069] Specifically, the energy recovery unit recovers target electric energy through energy recovery modes. The energy recovery modes include kinetic energy recovery mode, thermal energy recovery mode, and potential energy recovery mode. The target electric energy includes first target electric energy, second target electric energy, and third target electric energy. The first target electric energy is recovered through the kinetic energy recovery mode, the second target electric energy is recovered through the thermal energy recovery mode, and the third target electric energy is recovered through the potential energy recovery mode;

[0070] When the energy recovery unit recovers the first target electric energy, it recovers the first target electric energy through a kinetic energy recovery mode, and the kinetic energy recovery mode includes:

[0071] Step J01: Obtain the permanent magnet synchronous generator speed, track load, and DC bus voltage according to the track operation data, and use the permanent magnet synchronous generator speed, track load, and DC bus voltage as input variables;

[0072] Step J02: Use the excitation current adjustment amount and phase adjustment amount of the permanent magnet synchronous generator as output variables;

[0073] Step J03: Fuzzify the input variables and output variables according to the membership function to obtain fuzzy quantities;

[0074] Step J04: Formulate fuzzy control rules according to the expert evaluation method, and perform fuzzy inference on the fuzzy quantities according to the fuzzy control rules to obtain a fuzzy inference result;

[0075] Step J05: Defuzzify the fuzzy inference result according to the weighted average method and the target control weight. The defuzzification includes: setting the fuzzy inference results as the first fuzzy quantity A, the second fuzzy quantity B, and the third fuzzy quantity C respectively. The membership degree of the first fuzzy quantity A is the first membership degree a1, the weight of the first fuzzy quantity A is the first weight a`, the membership degree of the second fuzzy quantity B is the second membership degree b1, the weight of the second fuzzy quantity B is the second weight b`, the membership degree of the third fuzzy quantity C is the third membership degree c1, the weight of the third fuzzy quantity C is the third weight c`, and the target control weight is k1. According to the formula Calculate the precise control quantity yu;

[0076] Step J06: Input the precise control quantity into the excitation system and phase control system of the permanent magnet synchronous generator, and control the permanent magnet synchronous generator through the excitation system and phase control system of the permanent magnet synchronous generator to obtain the first target electric energy;

[0077] Step J07: Recover the first target electric energy to the energy recovery device.

[0078] Specifically, the kinetic energy recovery mode refers to the process of recovering the first target electric energy converted from kinetic energy, the heat energy recovery mode refers to the process of recovering the second target electric energy converted from heat energy, the potential energy recovery mode refers to the process of recovering the third target electric energy converted from thermal potential energy, the excitation current adjustment amount of the permanent magnet synchronous generator refers to the value that needs to be increased or decreased to control the excitation current of the permanent magnet synchronous generator, which is an accurate control quantity obtained by defuzzifying the fuzzy inference result, the phase adjustment amount refers to the angular value that needs to be changed to control the output current or voltage phase of the permanent magnet synchronous generator, which is an accurate control quantity obtained by defuzzifying the fuzzy inference result, and the membership function refers to the value for fuzzifying the input variable. In this embodiment, the acquisition method of the membership function is not limited, such as obtaining it based on statistical analysis. In this embodiment, the membership function is set as Among them, x is the actual measured value of the speed of the permanent magnet synchronous generator, μ(x) is the degree to which the speed x belongs to a certain fuzzy set. For example, for low speed, medium speed, and high speed, 0 < μ(x) < 1. ax is the value at which the membership degree starts to rise, bx is the starting point at which the membership degree rises to 1, cx is the end point at which the membership degree remains 1, and dx is the value at which the membership degree drops to 0. The fuzzification refers to converting the input variable into a fuzzy quantity according to the membership function. For example, converting the speed of the permanent magnet synchronous generator into three fuzzy quantities: low speed, medium speed, and high speed; converting the track load into three fuzzy quantities: light load, medium load, and heavy load; converting the DC bus voltage into three fuzzy quantities: low voltage, normal voltage, and high voltage; and converting the excitation current adjustment amount and phase adjustment amount into five fuzzy quantities: negative large, negative small, zero, positive small, and positive large. The expert evaluation method refers to the expert user with the ability to set the operation of the permanent magnet synchronous generator and the kinetic energy recovery system to set the fuzzy control rules. This embodiment does not limit the way of the expert evaluation method to give the fuzzy control rules. For example, the Internet expert uses the terminal to obtain the fuzzy control rules. The fuzzy control rule refers to the rule used to describe the relationship between the input variable and the output variable. This embodiment does not limit the fuzzy control rules. For example, set the fuzzy control rule that when the generator speed is high, the load torque is light, and the DC bus voltage is normal voltage, adjust the excitation current adjustment amount to positive small. The fuzzy inference refers to the process of determining the membership degree of the fuzzy quantity in each fuzzy set by taking the minimum value according to the fuzzy control rules. The defuzzification refers to turning the fuzzy inference result into specific excitation current adjustment values and phase adjustment values through the weighted average method. The weighted average method refers to the method of converting the fuzzy inference result into an accurate control quantity during the defuzzification process. The target control weight refers to the coefficient used to measure the importance degree of different fuzzy inference results during the defuzzification process in the kinetic energy recovery mode. This embodiment sets the target control weight k1 = 2. The first fuzzy quantity A refers to the fuzzy set obtained by fuzzifying a certain physical quantity related to control. For example, low speed, medium speed, and high speed. The second fuzzy quantity B refers to the fuzzy set obtained by fuzzifying another physical quantity related to control. For example, light load, medium load, and heavy load. The third fuzzy quantity C refers to the fuzzy set obtained by fuzzifying the physical quantity related to control that is different from the first fuzzy quantity A and the second fuzzy quantity B. For example, low voltage, normal voltage, and high voltage. The first membership degree a refers to the value corresponding to the first fuzzy quantity A, and its value range is between 0 and 1. The first weight a` refers to the coefficient used to measure the importance degree of the first fuzzy quantity A when determining the accurate control quantity. The second membership degree b refers to the value corresponding to the second fuzzy quantity B, and its value range is between 0 and 1. The second weight b` refers to the coefficient used to measure the importance degree of the second fuzzy quantity B when determining the accurate control quantity. The third membership degree c refers to the value corresponding to the third fuzzy quantity C, and its value range is between 0 and 1.The third weight c` refers to the coefficient used to measure the importance of the third fuzzy quantity C in determining the precise control quantity. The precise control quantity yu refers to the specific control parameter value obtained through comprehensive calculation of each fuzzy quantity, its membership degree, and weight. The excitation system of the permanent magnet synchronous generator refers to a device that indirectly achieves a similar excitation effect to that of a traditional generator by regulating the output current of the inverter. The phase control system refers to a device used to precisely adjust the phase of the output current or voltage of the permanent magnet synchronous generator.,

[0079] Specifically, the energy recovery unit precisely regulates through multiple steps, adjusts the permanent magnet synchronous generator based on the track operation data, efficiently recovers kinetic energy, and improves energy utilization efficiency.

[0080] Specifically, the energy recovery unit obtains the energy-saving requirement index A, compares the energy-saving requirement index A with the preset requirement index A0, judges the high or low situation of the energy-saving requirement index according to the comparison result, and adjusts the target control weight k1 according to the judgment result, where:

[0081] When A ≤ A0, the energy recovery unit determines that the high or low situation of the energy-saving requirement index is a low requirement and does not adjust the target control weight k1;

[0082] When A > A0, the energy recovery unit determines that the high or low situation of the energy-saving requirement index is a high requirement, adjusts the target control weight k1, and sets the adjusted target control weight as k1`, k1` = 1.5 × k1;

[0083] The energy recovery unit obtains the track transportation time T, compares the track transportation time T with the preset transportation time T0, judges the state of the track transportation time according to the comparison result, and updates the energy-saving requirement index A according to the judgment result, where:

[0084] When T ≤ T0, the energy recovery unit determines that the state of the track transportation time is a short time and does not update the energy-saving requirement index A;

[0085] When T > T0, the energy recovery unit determines that the state of the track transportation time is a long time, updates the energy-saving requirement index A, sets the update coefficient as α, 1 < α < 2, and the updated energy-saving requirement index is A`, A` = A × α.

[0086] Specifically, the preset requirement index A0 refers to the preset threshold for comparing with the energy-saving requirement index to judge the high or low situation of the energy-saving requirement index. In this embodiment, A0 = 0.2 is set. The preset transportation time T0 refers to the preset threshold for comparing with the track transportation time to judge the state of the track transportation time. In this embodiment, T0 = 25 min is set.

[0087] Specifically, the energy recovery unit dynamically adjusts the target control weight to optimize energy recovery, thereby increasing the target control weight k1, so as to make fuller use of the power generation capacity of the permanent magnet synchronous generator in the kinetic energy recovery mode, thereby further improving the energy-saving effect and the electric energy recovery efficiency.

[0088] Specifically, when the energy recovery unit recovers the second target electric energy, it recovers the second target electric energy through the heat energy recovery mode, and the heat energy recovery mode includes: the thermoelectric material converts the temperature difference generated by the conveyor belt into thermoelectric energy according to the Seebeck effect, and uses the thermoelectric energy as the second target electric energy, and recovers the second target electric energy to the energy recovery device;

[0089] When the energy recovery unit recovers the third target electric energy, it recovers the third target electric energy through the potential energy recovery mode, and the potential energy recovery mode includes: the potential energy possessed by the goods during transportation on the conveyor belt drives the permanent magnet synchronous generator to form an induced electromotive force and an induced current, obtaining the third target electric energy, and recovering the third target electric energy to the energy recovery device.

[0090] Specifically, the Seebeck effect refers to that when there is a temperature difference at both ends of the thermoelectric material, the charged particles inside the thermoelectric material move directionally, and finally a voltage difference is formed at both ends of the thermoelectric material, generating an electric current. The temperature difference generated by the conveyor belt refers to the fact that the local area of the conveyor belt generates heat due to friction with other components during the conveying process, resulting in a local temperature rise, forming a temperature difference with the non-friction area. The potential energy possessed by the goods during transportation on the conveyor belt refers to the height difference at different parts of the conveyor belt, so that the goods at a higher place have gravitational potential energy. The induced electromotive force refers to the electromotive force generated in a closed loop due to the change of magnetic flux in the electromagnetic induction phenomenon, providing power for the directional movement of charges, and making free charges move directionally to form an electric current under the condition of a closed loop.

[0091] Specifically, the energy recovery unit converts heat energy and potential energy into electric energy through the heat energy recovery mode and the potential energy recovery mode respectively, improving the energy utilization rate and contributing to energy conservation and emission reduction.

[0092] Specifically, the energy recovery unit coordinates the energy recovery mode according to the energy coordination method, and the energy coordination method includes:

[0093] Step Q01, set the first preset electric energy threshold as E k1， The second preset electric energy threshold is E k2， The third preset electric energy threshold is E k3 ;

[0094] Step Q02, compare the first target electric energy E1 with the first preset electric energy threshold E k1Compare them, judge the compliance status of the first target electric energy according to the comparison result, and control the kinetic energy recovery mode according to the judgment result, where:

[0095] When E1 > E k1 At this time, judge that the compliance status of the first target electric energy is compliant, control the kinetic energy recovery mode, and start the kinetic energy recovery mode;

[0096] When E1 ≤ E k1 At this time, judge that the compliance status of the first target electric energy is non-compliant, do not control the kinetic energy recovery mode, and do not start the kinetic energy recovery mode;

[0097] The energy recovery unit compares the second target electric energy E2 with the second preset electric energy threshold value E k2 Compare them, judge the compliance status of the second target electric energy according to the comparison result, and control the heat energy recovery mode according to the judgment result, where:

[0098] When E2 > E k2 At this time, judge that the compliance status of the second target electric energy is compliant, control the heat energy recovery mode, and start the heat energy recovery mode;

[0099] When E2 ≤ E k2 At this time, judge that the compliance status of the second target electric energy is non-compliant, do not control the heat energy recovery mode, and do not start the heat energy recovery mode;

[0100] The energy recovery unit compares the second target electric energy E2 with the second preset electric energy threshold value E k3 Compare them, judge the compliance status of the third target electric energy according to the comparison result, and control the potential energy recovery mode according to the judgment result, where:

[0101] When E3 > E k3 At this time, judge that the compliance status of the third target electric energy is compliant, control the potential energy recovery mode, and start the potential energy recovery mode;

[0102] When E3 ≤ E k3 At this time, judge that the compliance status of the third target electric energy is non-compliant, do not control the potential energy recovery mode, and do not start the potential energy recovery mode;

[0103] Step Q03, calculate the kinetic energy difference ΔE1 according to the formula ΔE1 = E1 - E k1 calculate the heat energy difference ΔE2 according to the formula ΔE2 = E2 - E k2 calculate the heat energy difference ΔE3 according to the formula ΔE3 = E3 - E k3 calculate the heat energy difference ΔE3 according to the formula ΔE = ΔE1 + ΔE2 + ΔE3 to calculate the total heat energy difference ΔE;

[0104] Step Q04: Obtain the adjustment coefficient k according to the expert evaluation method. Calculate the power ΔP1 to be adjusted for kinetic energy according to the formula ΔP1 = k×ΔE1, calculate the power ΔP2 to be adjusted for thermal energy according to the formula ΔP2 = k×ΔE2, calculate the power ΔP3 to be adjusted for potential energy according to the formula ΔP3 = k×ΔE3, and calculate the total adjusted power ΔP according to the formula ΔP = ΔP1 + ΔP2 + ΔP3;

[0105] Step Q05: Obtain the current working power P0 of the energy recovery device. Calculate the current kinetic energy working power P1 according to the formula P1 = P0 + ΔP1, calculate the current thermal energy working power P2 according to the formula P2 = P0 + ΔP2, calculate the current potential energy working power P3 according to the formula P3 = P0 + ΔP3, calculate the current total working power P according to the formula P = P1 + P2 + P3, and take the current total working power P as the energy recovery result;

[0106] Step Q06: Compare the current total working power P with the design power, where the design power includes the maximum design power P max and the minimum design power P min , determine the state of the current total working power according to the comparison result, and adjust the current total working power P according to the determination result, where:

[0107] When P ≤ P min , determine that the state of the current total working power P is low load, adjust the current total working power P, and adjust the current total working power P to the minimum design power P min ;

[0108] When P min <P ≤ P max , determine that the state of the current total working power P is normal, and do not adjust the current total working power P;

[0109] When P > P max , determine that the state of the current total working power P is overloaded, adjust the current total working power P, and adjust the current total working power P to the maximum design power P max；

[0110] Step Q07: Repeat steps Q01 to Q06 according to the recovery frequency k2.

[0111] Specifically, the first target electric energy E1 refers to the electric energy generated by the device through the kinetic energy recovery mode, the second target electric energy E2 refers to the electric energy generated by the device through the thermal energy recovery mode, the third target electric energy E3 refers to the electric energy generated by the device through the potential energy recovery mode, and the first preset electric energy threshold E k1 is a preset value used to judge the compliance state of the first target electric energy. In this embodiment, Ek1 = 60 KJ, the second preset electric energy threshold E k2 refers to a preset value used to judge the compliance status of the second target electric energy. In this embodiment, E k2 = 65 KJ, the third preset electric energy threshold E k3 refers to a preset value used to judge the compliance status of the third target electric energy. In this embodiment, E k3 = 70 KJ, the kinetic energy difference ΔE1 refers to the difference between the first target electric energy E1 and the first preset electric energy threshold E k1 . It reflects the gap between the current kinetic energy recovery electric energy and the expectation, and is used to judge whether the kinetic energy recovery mode meets the standard and subsequent power adjustment. The thermal energy difference ΔE2 refers to the difference between the second target electric energy E2 and the second preset electric energy threshold E k2 . It reflects the deviation between the current thermal energy recovery electric energy and the set standard, and assists in the control and power regulation of the thermal energy recovery mode. The potential energy difference ΔE3 refers to the difference between the third target electric energy E3 and the third preset electric energy threshold E k3 . It is used to measure whether the potential energy recovery electric energy reaches the expectation. The kinetic energy required adjustment power ΔP1 refers to the adjustment amount that needs to be made to the working power of the kinetic energy recovery mode to make the kinetic energy recovery electric energy meet the standard. The thermal energy required adjustment power ΔP2 refers to the adjustment amount that needs to be made to the working power of the thermal energy recovery mode to make the thermal energy recovery electric energy meet the standard. The potential energy required adjustment power ΔP3 refers to the adjustment amount that needs to be made to the working power of the potential energy recovery mode to make the potential energy recovery electric energy meet the standard. The current working power P0 of the energy recovery device refers to the power value consumed or output during real-time operation before the device performs energy coordination operation. The current kinetic energy working power P1 refers to the real-time working power of the energy recovery device in the kinetic energy recovery mode after considering the kinetic energy required adjustment power ΔP1. The current thermal energy working power P2 refers to the real-time working power of the energy recovery device in the thermal energy recovery mode after considering the thermal energy required adjustment power ΔP2. The current potential energy working power P3 refers to the real-time working power of the energy recovery device in the potential energy recovery mode after considering the potential energy required adjustment power ΔP3. The maximum design power P max refers to the maximum power upper limit determined during the design stage of the energy recovery device, which can operate safely and stably. When the total working power exceeds this value, the total working power needs to be adjusted to ensure the normal operation of the device. In this embodiment, the maximum design power P max is set by the expert evaluation method, and P max = 20 kW. The minimum design power P min refers to the minimum power value to maintain the basic operation function. In this embodiment, the minimum design power P min is set by the expert evaluation method, and P min= 10 kW. The recovery frequency refers to the number of times of converting the kinetic energy, thermal energy, and potential energy generated during the operation of the device into target electric energy within a preset duration. In this embodiment, the set recovery frequency k2 = 2 times / second, and the set preset duration is 1 min. The adjustment coefficient k refers to the parameter for balancing the relationship between the electric energy difference and the power to be adjusted. In this embodiment, the set adjustment coefficient k = 0.2.

[0112] Specifically, the energy release unit regulates the recovery mode through the energy release control rule formulation method, so that the total power adapts to the design range, thereby efficiently and stably recovering energy.

[0113] Specifically, the energy release unit compares the track load P` with each preset track load power. The preset track load powers include the first preset track load power P`1 and the second preset track load P`2, and judges and outputs the demand level of the track load P` according to the comparison result. Among them:

[0114] When P` ≤ P`1, the output of the demand level of the track load P` is low demand;

[0115] When P`1 < P` ≤ P`2, the output of the demand level of the track load P` is medium demand;

[0116] When P` > P`2, the output of the demand level of the track load P` is high demand;

[0117] The energy release unit formulates the energy release control rule according to the demand level through the energy release control rule formulation method. The energy release control rule formulation method includes:

[0118] When the demand level of the track load P` is low demand, the energy recovery result is used for small current floating charge of the energy storage device;

[0119] When the demand level of the track load P` is medium demand, the energy recovery result is used for power supplement of the energy storage device;

[0120] When the demand level of the track load P` is high demand, the energy recovery result is used for the track load;

[0121] The energy release unit obtains the current device operating temperature through a temperature sensor, compares the current device operating temperature W with the preset temperature W0, judges the state of the current device operating temperature according to the comparison result, and adjusts the track load P` according to the judgment result. Among them:

[0122] When W ≤ W0, the energy release unit determines that the current device operating temperature state is a low temperature state and does not adjust the track load P`.

[0123] When W > W0, the energy release unit determines that the current device operating temperature state is a high temperature state, adjusts the track load P`, sets the track adjustment coefficient as α3, α3 = 0.6 - 1.3e -1.25×[(W-W0)+2] , and the updated track load power is P``, P`` = α3 × P`.

[0124] The energy release unit obtains the device torque N through a torque sensor, compares the device torque N with the preset torque N0, judges the device torque state according to the comparison result, and updates the current device operating temperature W according to the judgment result, where:

[0125] When N ≤ N0, the energy release unit determines that the device torque state is low torque and does not update the current device operating temperature W.

[0126] When N > N0, the energy release unit determines that the device torque state is high torque, updates the current device operating temperature W, sets the update coefficient as θ, and the updated current device operating temperature is W`, W` = θ × W.

[0127] Specifically, the track load refers to the total power consumed by the track due to carrying the device and the goods during the device's operation of transporting goods. The track load is obtained through a pressure sensor to reflect the instantaneous energy demand status of the track. The small - current floating charge means continuously charging the energy storage device with a current less than the normal charging current to maintain the charge of the energy storage device. The charge replenishment means charging the energy storage device's charge with the normal charging current. The normal charging current refers to the current value range determined based on the device's design parameters, battery characteristics, and relevant industry standards under the premise of meeting the device's charging requirements and ensuring charging safety. The preset temperature W0 is a preset threshold for comparing with the current device operating temperature to judge the state of the current device operating temperature. In this embodiment, W0 = 25°C. The preset torque N0 is a preset threshold for comparing with the device torque to judge the device torque state. The first preset track load power P`1 refers to the lower threshold of the track load power demand for maintaining the normal operation of the track. In this implementation, P`1 = 30kW. The second preset track load P`2 refers to the upper threshold of the track load power demand for maintaining the normal operation of the track. In this embodiment, P`2 = 80kW. The device torque N refers to the measure of the force generated by each component during the device's operation to cause its rotation.

[0128] Specifically, the torque of the energy release unit device and the current device operating temperature are used to adjust the track load, so as to accurately match the energy supply required by the track load, thereby further improving the energy utilization and the reliability of equipment operation.

[0129] Specifically, the change prediction unit constructs an energy change prediction model according to the energy change prediction model construction method, and the energy change prediction model construction method includes:

[0130] Step S01: Divide the historical energy prediction data into a 70% prediction training set, a 20% prediction validation set, and a 10% prediction test set;

[0131] Step S02: Select the long short-term memory network model as the network architecture of the energy change prediction model, initialize the weights and biases of the long short-term memory network model to obtain a preliminary energy change prediction model;

[0132] Step S03: For each convolutional layer in the parameters of the preliminary energy change prediction model, use 32 convolutional kernels with a size of 3×3. Select the Adam optimizer and the cross-entropy loss function to train the preliminary energy change prediction model. Load the prediction training set into the preliminary energy change prediction model, perform forward propagation through the preliminary energy change prediction model, and calculate the output value of the preliminary energy change prediction model;

[0133] Step S04: Input the prediction validation set into the preliminary energy change prediction model, calculate the loss function value based on the output value and the true value of the preliminary energy change prediction model, calculate the gradient through the backpropagation algorithm, and update the weights and biases of the preliminary energy change prediction model. Repeat the processes of forward propagation, loss calculation, and backpropagation to obtain a trained energy change prediction model.

[0134] Step S04: Input the prediction test set into the trained energy change prediction model for testing, output the test accuracy rate R`. Use the trained energy change prediction model with the test accuracy rate R` reaching 90% as the energy change prediction model for output.

[0135] Specifically, the historical energy prediction data refers to the orbital operation data and device operation data generated by the historical operation of the device as input data input into the energy change prediction model to obtain the energy change trend result corresponding to the orbital operation data and device operation data generated by the historical operation of the device. The long short-term memory network model refers to a special recurrent neural network that processes sequential data, filters, stores, and forgets information. The initialization means that the true value refers to the load change trend corresponding to the orbital operation data and device operation data generated by the historical operation of the device. The weight refers to the parameter in the long short-term memory network model used to measure the connection strength between neurons. The bias refers to the parameter in the long short-term memory network model that enables the neuron to have a certain output even when the input is zero. The convolution kernel refers to the key component in the long short-term memory network model that slides on the input data for convolution operation, multiplies elements with the local area of the input data and sums them, maps the input data to a new feature space, and thus extracts various features in the data. Different convolution kernels can extract different types of features. The Adam optimizer refers to an algorithm used to update the weights and biases of the network. The cross-entropy loss function refers to calculating the loss for the classification task by comparing the difference between the probability distribution predicted by the model and the probability distribution of the true label. The loss function value refers to the value obtained by calculating the loss function to measure the degree of difference between the model prediction result and the true result. The backpropagation algorithm refers to propagating the gradient of the loss function backward along the network, calculating the parameter gradients of each layer in turn, and finally updating the parameters of the model according to these gradients.

[0136] Specifically, when the change prediction unit adjusts the energy coordination method according to the energy change prediction model, the process is as follows:

[0137] Taking the orbital load power, DC bus voltage, device voltage, device current, and current device operating temperature in the orbital operation data as the model input data, inputting the model input data into the energy change prediction model, and outputting the energy change trend result by the energy change prediction model. The energy change trend result includes a load reduction trend and a load increase trend, and the recovery frequency k2 is optimized according to the energy change trend result;

[0138] When the energy change trend result is a load reduction trend, the recovery frequency k2 is optimized. The optimization coefficient is set as α1, 0 < α1 < 1, and the optimized recovery frequency is k2`, k2` = α1 × k2;

[0139] When the energy change trend result is a load increase trend, the recovery frequency k2 is optimized. The optimization coefficient is set as α2, 1 < α2 < 2, and the optimized recovery frequency is k2``, k2`` = α2 × k2.

[0140] Specifically, the load reduction trend means that from the current moment on, the total pressure and power consumption brought by goods on the track and the like continuously decrease, and the load increase trend means that from the current moment on, the total pressure and power consumption brought by goods on the track and the like continuously increase.

[0141] Specifically, the change prediction unit optimizes the recovery frequency according to the result of the energy change situation, so as to dynamically regulate the energy coordination method to fit the change of the track load, thereby further improving the energy recovery and utilization efficiency.

[0142] Specifically, the fault adjustment unit constructs a continuous data prediction model according to the continuous data prediction model construction method, and the continuous data prediction model construction method includes:

[0143] Step V01: Divide the historical energy data into a 70% energy training set, a 20% energy verification set, and a 10% energy test set;

[0144] Step V02: Set hidden_size in the parameters of the recurrent neural network model to 128 and num_layers to 4 layers, and input the energy training set into the recurrent neural network model with the parameters set for training;

[0145] Step V03: Input the energy verification set into the trained recurrent neural network model to optimize the parameters of the trained recurrent neural network model, then input the energy test set into the recurrent neural network model with the optimized parameters for testing, and output the test accuracy rate R. Output the recurrent neural network model with the optimized parameters whose test accuracy rate R reaches 90% as the continuous data prediction model;

[0146] The fault adjustment unit inputs the track load power, DC bus voltage, device voltage, device current, and current device operating temperature into the continuous data prediction model, and outputs the corresponding parameter data within the preset prediction coverage duration Ty output by the continuous data prediction model as the continuous data prediction model result, and uses the corresponding parameter data within the preset prediction coverage duration Ty as supplementary data to supplement the missing data of the track operation data.

[0147] Specifically, the historical energy data refers to the orbital operation data and device operation data generated by the historical operation of the device as input data input into a continuous data prediction model, obtaining the corresponding data sequence within the coverage duration Ty of the orbital operation data and device operation data generated by the historical operation of the device, the orbital operation data and device operation data generated by the historical operation of the device, and the corresponding data sequence of the orbital operation data and device operation data generated by the historical operation of the device. The parameter optimization refers to the process of adjusting the weights and biases of the model to make the performance of the model on the validation set reach the optimal when using the energy validation set to evaluate the trained recurrent neural network model. The preset prediction coverage duration Ty refers to the corresponding parameter data set that can cover a specific missing time length obtained by predicting the orbital load power, DC bus voltage, device voltage, device current, and current device operating temperature parameters through the continuous data prediction model.

[0148] Specifically, the fault adjustment unit constructs a continuous data prediction model to supplement the missing orbital operation data, improve data integrity, and enable the system to operate efficiently and stably based on accurate data.

[0149] Specifically, the fault adjustment unit obtains the ratio of the number of faulty devices to the number of devices operating normally, takes it as the fault ratio H, and compares the fault ratio H with the preset fault ratio H0. According to the comparison result, it judges the fault ratio situation and adjusts the preset prediction coverage duration Ty according to the judgment result, where:

[0150] When H ≤ H0, the fault adjustment unit determines that the fault ratio situation is a low ratio and does not adjust the preset prediction coverage duration Ty:

[0151] When H > H0, the fault adjustment unit determines that the fault ratio situation is a high ratio, adjusts the preset prediction coverage duration Ty, sets the prediction update coefficient as α4, α4 = 0.5 - 1.2e -1.25×[(H0-H)+2] , and the updated current prediction coverage duration is Ty`, Ty` = α4 × Ty;

[0152] The fault adjustment unit obtains the average number of faults g1 that occur per day during normal operation of the device and the number of faults g2 at the current moment, and calculates the fault sudden increase ratio g according to the formula g = ((g2 - g1) / g1) × 100%;

[0153] The fault adjustment unit compares the fault sudden increase ratio g with the preset fault sudden increase ratio g0, judges the state of the fault sudden increase ratio according to the comparison result, and updates the fault ratio H according to the judgment result, where:

[0154] When g ≤ g0, the fault adjustment unit determines that the state of the fault sudden increase ratio is normal and does not update the fault ratio H;

[0155] When g > g0, the fault adjustment unit determines that the state of the fault sudden increase ratio is abnormal, updates the fault proportion H, and sets the fault update coefficient as β3, where β3 = 1.48 - 0.4e -0.7×(g-g0) , and the updated fault proportion is H`, where H` = β3 × H.

[0156] Specifically, the preset fault proportion H0 refers to a threshold preset for comparing with the actually calculated fault proportion H to determine the proportion of the current number of faulty devices in the total number of devices. In this embodiment, H0 = 20%. The preset fault sudden increase proportion g0 refers to a threshold preset for comparing with the fault sudden increase ratio to judge the state of the fault sudden increase ratio. In this embodiment, g0 = 250%.

[0157] Specifically, the fault adjustment unit dynamically adjusts the preset prediction coverage duration Ty through the fault sudden increase ratio and the fault proportion to facilitate timely response to fault changes, thereby further improving the fault response accuracy and system stability.

[0158] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An orbital operation device based on energy conservation and power saving, characterized in that Including: A collection node group, which is connected to a conveyor belt and is used for collecting track operation data and device operation data; An energy recovery device, which is connected to an intelligent module and a conveyor belt and is used for recovering and storing target energy; An intelligent module, which is connected to an energy recovery device and a conveyor belt and is used for intelligently controlling the device. The intelligent control refers to the process of controlling the device according to track operation data and device operation data; A conveyor belt, which is connected to a collection node group, an energy recovery device, an intelligent module, and a permanent magnet synchronous generator and is used for conveying goods; A permanent magnet synchronous generator, which is connected to a conveyor belt and an energy storage device and is used for converting kinetic energy into a first target electric energy and converting potential energy into a third target electric energy; An energy storage device, which is connected to a permanent magnet synchronous generator and is used for supplying power to the device.

2. The track operation device based on energy conservation and power saving according to claim 1, wherein: A data acquisition unit for acquiring track operation data and device operation data; An energy recovery unit for recovering target electric energy according to track operation data through an energy recovery mode, and also for coordinating the energy recovery mode according to an energy coordination method to obtain an energy recovery result; An energy release unit for dividing the track load requirements according to track operation data to obtain a requirement division level, and formulating an energy release control rule according to the energy recovery result through an energy release control rule formulation method; A change prediction unit for constructing an energy change prediction model according to an energy change prediction model construction method, and optimizing the energy coordination method according to the energy change prediction model; A fault adjustment unit for constructing a continuous data prediction model according to a continuous data prediction model construction method, and supplementing missing data in track operation data according to the continuous data prediction model.

3. The rail operation device based on energy saving according to claim 2, characterized in that, The energy recovery unit recovers target electric energy through an energy recovery mode. The energy recovery mode includes a kinetic energy recovery mode, a heat energy recovery mode, and a potential energy recovery mode. The target electric energy includes a first target electric energy, a second target electric energy, and a third target electric energy. The first target electric energy is recovered through the kinetic energy recovery mode, the second target electric energy is recovered through the heat energy recovery mode, and the third target electric energy is recovered through the potential energy recovery mode; When the energy recovery unit recovers the first target electric energy, the first target electric energy is recovered through the kinetic energy recovery mode.

4. The rail operation device based on energy conservation and power saving according to claim 3, characterized in that, The energy recovery unit obtains an energy saving requirement index A, compares the energy saving requirement index A with a preset requirement index A0, judges the high or low situation of the energy saving requirement index according to the comparison result, and adjusts the target control weight k1 according to the judgment result.

5. The rail operation device based on energy conservation and power saving according to claim 4, characterized in that, When the energy recovery unit recovers the second target electric energy, the second target electric energy is recovered through the heat energy recovery mode. The heat energy recovery mode includes: a thermoelectric material converts the temperature difference generated by the conveyor belt into a thermoelectric power according to the Seebeck effect, takes the thermoelectric power as the second target electric energy, and recovers the second target electric energy to the energy recovery device; When the energy recovery unit recovers the third target electric energy, it recovers the third target electric energy through the potential energy recovery mode. The potential energy recovery mode includes: driving a permanent magnet synchronous generator to form an induced electromotive force and an induced current by the potential energy possessed by the goods during transportation on the conveyor belt, obtaining the third target electric energy, and recovering the third target electric energy to the energy recovery device.

6. The rail operation device based on energy saving according to claim 5, wherein, The energy recovery unit coordinates the energy recovery mode according to the energy coordination method to obtain an energy recovery result.

7. The rail operation device based on energy conservation and power saving according to claim 2, characterized in that, The energy release unit compares the track load P` with each preset track load power. The preset track load powers include the first preset track load power P`1 and the second preset track load P`2. It is set that P`1 = 30kW and P`2 = 80kW. And it judges and classifies the demand of the track load P` according to the comparison result, and outputs the classified demand of the track load P` according to the judgment result. The energy release unit formulates the energy release control rule according to the demand classification level through the energy release control rule formulation method. The energy release unit obtains the current device operating temperature through a temperature sensor, compares the current device operating temperature W with the preset temperature W0, judges the state of the current device operating temperature according to the comparison result, and adjusts the track load P` according to the judgment result.

8. The rail operation device based on energy conservation and power saving according to claim 2, characterized in that, The change prediction unit constructs an energy change prediction model according to the energy change prediction model construction method.

9. The track running device based on energy saving according to claim 8, wherein When the change prediction unit adjusts the energy coordination method according to the energy change prediction model, the process is as follows: Taking the track load power, DC bus voltage, device voltage, device current, and current device operating temperature in the track operation data as the model input data, inputting the model input data into the energy change prediction model, and the energy change prediction model outputs the energy change trend result. The energy change trend result includes the load lightening trend and the load increasing trend, and optimizes the recovery frequency k2 according to the energy change trend result.

10. The track running device based on energy saving according to claim 2, characterized in that, The fault adjustment unit inputs the track load power, DC bus voltage, device voltage, device current, and current device operating temperature into the continuous data prediction model, outputs the corresponding parameter data within the preset prediction coverage duration Ty output by the continuous data prediction model as the continuous data prediction model result, and uses the corresponding parameter data within the preset prediction coverage duration Ty as supplementary data to supplement the missing data of the track operation data. The fault adjustment unit obtains the ratio of the number of faulty devices to the number of normally operating devices, takes it as the fault ratio H, compares the fault ratio H with the preset fault ratio H0, judges the fault ratio situation according to the comparison result, and adjusts the preset prediction coverage duration Ty according to the judgment result.

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