Intelligent control method and control system for electric tensioner

Abstract

The application discloses an intelligent control method and control system of an electric tension machine, and relates to the technical field of electric tension machines, which comprises the following steps: connecting an electric tension machine set and an electric small traction machine with a battery container respectively, and connecting the electric tension machine set and the electric small traction machine with a centralized control module in communication respectively; continuously collecting the wire state of the electric tension machine set and working condition data, obtaining a wire tension-displacement monitoring data sequence set and a working condition data sequence set; continuously collecting the battery state of the battery container, obtaining a battery state data sequence; determining an electric tension machine cooperative control scheme in combination with the aforementioned collected information, and cooperatively controlling the electric tension machine set. The application solves the technical problem that the cooperative control and energy dynamic scheduling between multiple electric tension machines and electric traction machines cannot be realized in the prior art, and achieves the technical effect of guaranteeing the stability of tension while optimizing the energy recovery and distribution efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric tensioner, in particular to an intelligent control method and control system of electric tensioner. BACKGROUND

[0002] In the process of tension field construction, multiple electric tensioners and electric traction machines are usually used to complete the wire deployment and rope traction operation, but the running states of each device are separated and lack unified control mechanism. Although the tensioner can recover electric energy in passive working condition, due to the lack of efficient centralized management and deployment mechanism, the recovered electric energy is difficult to form an effective closed loop with the energy consumption of the traction machine, resulting in low energy utilization rate. At the same time, there are problems such as inconsistent output power and uncoordinated tension response among different tensioners in actual operation, which makes it difficult to maintain stable wire tension, increases the operation risk and equipment energy consumption. SUMMARY

[0003] The present application provides an intelligent control method and control system of electric tensioner, which is used to solve the technical problem that the existing technology cannot realize the cooperative control and energy dynamic scheduling between multiple electric tensioners and electric traction machines.

[0004] In view of the above problems, the present application provides an intelligent control method and control system of electric tensioner.

[0005] In a first aspect of the present application, an intelligent control method of electric tensioner is provided, which comprises:

[0006] The electric tensioner set and one electric small traction machine are connected with the battery container through the high-voltage bus, and the electric tensioner set and one electric small traction machine are connected with the centralized control module through the CAN bus network; the wire state of the electric tensioner set is continuously collected by using the tension sensor and the displacement sensor, and a wire tension-displacement monitoring data sequence set is obtained; the working condition data sequence set of the electric tensioner set is obtained by using the centralized control module according to the preset electric tensioner working condition index; the battery state data sequence is obtained by continuously collecting the battery state of the battery container through the battery management system; the basic information of the electric small traction machine is obtained, the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set.

[0007] In a second aspect of the present application, an intelligent control system of electric tensioner is provided, which comprises:

[0008] The connecting module is used for connecting the electric tensioner set and the electric small traction machine with the battery container through a high-voltage bus, and connecting the electric tensioner set and the electric small traction machine with the centralized control module through a CAN bus network; the state acquisition module is used for continuously acquiring the wire state of the electric tensioner set by using a tension sensor and a displacement sensor, and obtaining a wire tension-displacement monitoring data sequence set; the working condition acquisition module is used for acquiring working condition data of the electric tensioner set according to a preset electric tensioner working condition index by using the centralized control module, and obtaining a working condition data sequence set; the battery state acquisition module is used for continuously acquiring the battery state of the battery container by using a battery management system, and obtaining a battery state data sequence; the cooperative control module is used for acquiring basic information of the electric small traction machine, determining an electric tensioner cooperative control scheme in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and transmitting the electric tensioner cooperative control scheme to the centralized control module to cooperatively control the electric tensioner set.

[0009] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0010] The connecting module is used for connecting the electric tensioner set and the electric small traction machine with the battery container through a high-voltage bus, and connecting the electric tensioner set and the electric small traction machine with the centralized control module through a CAN bus network; the state acquisition module is used for continuously acquiring the wire state of the electric tensioner set by using a tension sensor and a displacement sensor, and obtaining a wire tension-displacement monitoring data sequence set; the working condition acquisition module is used for acquiring working condition data of the electric tensioner set according to a preset electric tensioner working condition index by using the centralized control module, and obtaining a working condition data sequence set; the battery state acquisition module is used for continuously acquiring the battery state of the battery container by using a battery management system, and obtaining a battery state data sequence; the cooperative control module is used for acquiring basic information of the electric small traction machine, determining an electric tensioner cooperative control scheme in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and transmitting the electric tensioner cooperative control scheme to the centralized control module to cooperatively control the electric tensioner set. The application solves the technical problem that multiple electric tensioners and electric traction machines cannot be cooperatively controlled and energy dynamically scheduled in the prior art, generates a cooperative control scheme in real time by fusing tension-displacement monitoring, battery state monitoring and working condition data analysis based on the centralized control architecture of the CAN bus, and achieves the technical effect of guaranteeing tension stability while optimizing energy recovery and distribution efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0012] Figure 1 A flow chart of an intelligent control method of an electric tension machine provided by the embodiment of the present application is shown in the figure.

[0013] Figure 2 A structure diagram of an intelligent control system of an electric tension machine provided by the embodiment of the present application is shown in the figure.

[0014] Legend: connection module 11, state acquisition module 12, working condition acquisition module 13, battery state acquisition module 14, cooperative control module 15. DETAILED DESCRIPTION

[0015] The present application provides an intelligent control method and control system of an electric tension machine, aiming at solving the technical problem that the existing technology cannot realize the cooperative control and energy dynamic scheduling between multiple electric tension machines and electric traction machines. Through the centralized control architecture based on CAN bus, the tension-displacement monitoring, battery state monitoring and working condition data analysis are integrated to generate a cooperative control scheme in real time, so as to achieve the technical effect of ensuring the stability of tension while optimizing the energy recovery and distribution efficiency.

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort belong to the scope of protection of the present application.

[0017] It should be noted that any variation of the terms "comprise" and "have" is intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment one, as shown in the figure, the present application provides an intelligent control method of an electric tension machine, the method comprises: Figure 1

[0019] ​Step S100: connect the set of electric tensioners and the electric small tractor to the battery container through the high-voltage bus, and connect the set of electric tensioners and the electric small tractor to the centralized control module through the CAN bus network.

[0020] In the embodiment of the application, the three electric tensioners in the set of electric tensioners and the high-voltage direct-current end of the electric small tractor are connected to the battery container through the high-voltage bus, realizing high-voltage direct-current power transmission between the above-mentioned devices and the battery container.

[0021] At the same time, the control interfaces of the three electric tensioners and the electric small tractor are connected to the centralized control module through the CAN bus network, a control architecture based on the CAN network is constructed, and the unified collection and command issuing of the operating parameters of the devices are realized. The connection process completes the synchronous integration of the tension field multi-device in the power supply and control instruction level, and provides bottom support for subsequent collaborative scheduling and intelligent control.

[0022] Step S200: continuously collect the wire state of the set of electric tensioners by using the tension sensor and the displacement sensor, and obtain a set of wire tension-displacement monitoring data sequences.

[0023] In the embodiment of the application, for the three electric tensioners in the set of electric tensioners, a strain tension sensor is installed on the tension wheel structure to continuously collect the deformation caused by the force of the tension wheel and convert it into wire tension data. At the same time, a rotary encoder type displacement sensor is arranged at the outlet path of the wire, and the change of the wire length is continuously measured by using the encoder pulse counting method to obtain displacement data. The data collected by the tension sensor and the displacement sensor are synchronously read at a fixed sampling period, and are time-stamped and numbered, to construct the tension-displacement monitoring data sequence of the three electric tensioners, and to aggregate the wire tension-displacement monitoring data sequence set.

[0024] Step S300: collect the working condition data of the set of electric tensioners according to the preset electric tensioner working condition index by using the centralized control module, and obtain a set of working condition data sequences.

[0025] Further, the method provided by the application embodiment further comprises:

[0026] The preset electric tensioner working condition index at least includes output tension, rotating speed, and output power.

[0027] In this embodiment, the centralized control module first issues operating data collection instructions to the three electric tension machines via the CAN bus network, activating the tension monitoring process within each device. During the output tension collection process, strain gauge tension sensors mounted on the tension pulleys monitor the changes in the tension applied by the conductors to the tension pulleys in real time. The tension data collected by the sensors is continuously read during each sampling cycle and time-stamped and numbered by the centralized control module to form a tension data sequence corresponding to each tension machine.

[0028] For speed data collection, the centralized control module synchronously reads the encoder signals on the three tension machine main drive motors. The encoders record the number of motor shaft rotations per unit time, thereby determining the current motor speed. The collected speed data is recorded at a consistent interval, with each piece of data accompanied by a device number and timestamp, ensuring synchronization and comparability of the collected operating indicators.

[0029] During output power acquisition, the centralized control module establishes communication with the three electric tensioners in the electric tensioner cluster via the CAN bus and collects operating data based on preset operating conditions for the electric tensioners. These conditions include output tension, speed, and output power.

[0030] To collect output tension, the centralized control module instructs the strain gauge tension sensor integrated into the tension pulley assembly to continuously monitor changes in the tension applied by the conductor to the tension pulley. The raw signal from each tension sensor on the electric tensioner is read via an internal interface and transmitted to the centralized control module, where it is organized into time series to construct tension data subsequences.

[0031] To collect speed data, the centralized control module obtains the current motor speed data from the incremental rotary encoder on the shaft end of each electric tensioner's main motor. The encoder's pulse signal reflects the dynamic process of motor shaft rotation. By periodically reading the encoder output, the acquired speed value is timestamped and written into the speed data subsequence.

[0032] To collect output power, the centralized control module acquires voltage and current sampling signals during motor operation through a communication interface with the tension machine inverter. It then uses the tension machine's internal power detection circuit to output the actual operating power. This data directly reflects the equipment's energy consumption under varying loads. The collected data is then organized into a chronological power data subsequence within the centralized control module.

[0033] Finally, the centralized control module uses the equipment number as the index to uniformly time-align and structurally integrate the three sub-sequences of tension, speed and power to form a complete working condition data sequence for each electric tension machine, and aggregates the working condition data of the three devices into a working condition data sequence set of the electric tension machine set.

[0034] Step S400: continuously collecting battery state of the battery container by the battery management system to obtain a battery state data sequence.

[0035] In the embodiments of the present application, when the battery management system continuously collects the battery state of the battery container, first, the voltage acquisition line configured inside the battery container is used to periodically measure all series-connected battery cells to obtain the real-time voltage value of each battery cell. Secondly, multiple temperature sensors are arranged in the battery container to read the temperature output value of each sensor at regular intervals to obtain the temperature distribution of the battery during operation and reflect the thermal characteristic changes of the battery at different positions. Then, a current detection device is configured on the battery charging and discharging path to continuously collect the current change during charging and discharging to obtain the current data of the battery at different operating stages. Finally, the state of charge of the battery pack is calculated by using the integral accumulation method according to the collected voltage and current data; the pressure difference level is calculated by comparing the difference between the maximum cell voltage and the minimum cell voltage; and the current remaining capacity is calculated according to the accumulated discharge current and discharge time. The above data is recorded in chronological order to form a battery state data sequence.

[0036] Step S500: obtaining basic information of the electric traction machine, combining the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence to determine an electric tensioner cooperative control scheme, and transmitting the electric tensioner cooperative control scheme to the centralized control module to cooperatively control the electric tensioner set.

[0037] In the embodiment of the present application, first, the basic information of the electric small tractor is obtained, which refers to the minimum working electric energy required by the tractor to complete the traction task under the current working condition. This information is obtained by the electric small tractor according to the actual operating parameters. Specifically, first, the traction force value required by the traction wire rope is collected in real time, which is obtained by the tension sensor installed on the traction wheel. At the same time, the rotation speed of the traction wheel is collected, and the current operating speed is measured by the encoder. Then, combined with the traction duration, the energy demand in the traction process is calculated. The energy calculation is based on the power value composed of the traction force and the operating speed, and multiplied by the time length required for the traction operation, so as to obtain the minimum working electric energy required by the tractor under the current task. Then, based on the basic information of the electric small tractor, the electric energy storage limit value of the battery container is identified to determine the battery container electric energy storage constraint. Then, the tension-displacement abnormal feature set is determined by identifying the tension-displacement abnormal features of the wire tension-displacement monitoring data sequence set. At the same time, the electric tensioner adjustment interlock scale is determined by traversing the working condition data sequence set and identifying the synchronization deviation. On this basis, the battery state abnormal features are extracted according to the battery container electric energy storage constraint and the battery state data sequence. Finally, the electric tensioner cooperative control scheme is determined by comprehensively analyzing the tension-displacement abnormal feature set, the battery state abnormal features and the electric tensioner adjustment interlock scale.

[0038] Finally, after the electric tensioner cooperative control scheme is formed, the electric tensioner cooperative control scheme is transmitted to the electric tensioner set managed by the centralized control module, and the output tension, rotation speed and power of the three electric tensioners are synchronously adjusted to complete the cooperative control of the electric tensioner set.

[0039] Further, in the method provided by the application embodiment, the basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, which further comprises:

[0040] The battery container electric energy storage constraint is determined based on the basic information of the electric small tractor. The tension-displacement abnormal feature set is determined by identifying the tension-displacement abnormal features of the wire tension-displacement monitoring data sequence set. The electric tensioner adjustment interlock scale is determined by traversing the working condition data sequence set and identifying the synchronization deviation. The battery state abnormal features are extracted according to the battery container electric energy storage constraint and the battery state data sequence. The electric tensioner cooperative control scheme is determined by analyzing the tension-displacement abnormal feature set, the battery state abnormal features and the electric tensioner adjustment interlock scale.

[0041] In the embodiment of the present application, firstly, the battery container electric energy storage limit is identified based on the basic information of the electric traction machine. Specifically, the centralized control module obtains the current reported traction force, traction speed and operation duration of the traction machine, and calculates using the energy demand conversion method. This method obtains the traction power by multiplying the traction force and the traction speed, and then multiplies the traction power and the operation time to convert the minimum working electric energy required to complete the traction operation. The minimum working electric energy is the basic information of the electric traction machine. The minimum working electric energy is used as a judgment reference to compare with the current battery state of charge in the battery state data sequence, to identify the maximum effective electric quantity currently available for the battery container, and finally determine the electric energy storage constraint of the battery container.

[0042] Next, the tension-displacement abnormal feature of the conductor tension-displacement monitoring data sequence set is identified. In this process, a conductor tension-displacement monitoring data sequence is randomly extracted from the conductor tension-displacement monitoring data sequence set, and an identification method containing multiple identification channels is used to identify and process the conductor tension-displacement monitoring data sequence, and the corresponding abnormal feature set is extracted. Subsequently, the abnormal feature set is mapped and interactively analyzed to determine the tension-displacement abnormal feature. The above identification and mapping process is performed on all conductor tension-displacement monitoring data sequences one by one until all data in the conductor tension-displacement monitoring data sequence set are identified, thereby obtaining the tension-displacement abnormal feature set.

[0043] Subsequently, the synchronous deviation is identified by traversing the working condition data sequence set. In this process, first, the working condition data sequence set is synchronously mapped and associated to construct a synchronous mapping working condition data sequence chain. Then, the synchronous mapping working condition data sequence chain is traversed one by one to identify the deviation between the electric tension machines in output tension, speed and output power, forming a deviation set sequence. Then, the mean value of the deviation set sequence is calculated to form a deviation mean value set, and data inversion analysis is performed based on the deviation mean value set to determine the electric tension machine adjustment interlocking scale.

[0044] Next, the battery state abnormal feature is identified according to the battery container electric energy storage constraint and the battery state data sequence. In this process, the battery container electric energy storage constraint and the battery state data sequence are identified by the pre-constructed battery state abnormal feature identifier to obtain the battery state abnormal feature.

[0045] Finally, the interlocking scale of the electric tensioner is adjusted according to the tension-displacement abnormal feature set, the battery state abnormal feature, and the electric tensioner adjustment parameter set is determined based on the tension-displacement abnormal feature set and the battery state abnormal feature, which is used to guide the tension output and power adjustment of each tensioner. Then, the electric tensioner adjustment parameter set is corrected according to the electric tensioner adjustment interlocking scale, so as to ensure that the output adjustment of the multiple electric tensioners meets the linkage constraint, and finally the electric tensioner cooperative control scheme is generated.

[0046] Further, in the method provided by the application, the tension-displacement abnormal feature set is determined by identifying the tension-displacement abnormal features of the wire tension-displacement monitoring data sequence set, and the method further comprises the following steps:

[0047] Step a: the first wire tension-displacement monitoring data sequence randomly extracted from the wire tension-displacement monitoring data sequence set is identified by using the identification channel set of the multi-channel tension-displacement abnormal feature identifier, and a first multi-channel tension-displacement abnormal feature set is determined; step b: the first multi-channel tension-displacement abnormal feature set is mapped and interacted, and a first tension-displacement abnormal feature is determined; steps a-b are repeated, and after the wire tension-displacement monitoring data sequence in the wire tension-displacement monitoring data sequence set is identified, the tension-displacement abnormal feature set is obtained.

[0048] In the embodiments of the application, in step a, a group of first wire tension-displacement monitoring data sequences randomly extracted from the wire tension-displacement monitoring data sequence set are identified by using the identification channel set of the multi-channel tension-displacement abnormal feature identifier. The identification channel set is a plurality of time scale windows established based on the historical wire abnormal interval time and the displacement abnormal interval time, and each identification channel corresponds to an abnormal response mode, such as high-frequency tension disturbance, low-amplitude displacement delay, etc. In the identification process, each channel analyzes the local feature change in the first wire tension-displacement monitoring data sequence in parallel, extracts abnormal feature data corresponding to its scale, and then forms a first multi-channel tension-displacement abnormal feature set.

[0049] In step b, the first multi-channel tension-displacement anomaly feature set is subjected to multi-channel mapping interaction. Specifically, first, an enumeration combination operation is performed on the first multi-channel tension-displacement anomaly feature set to construct a first multi-channel enumeration combination set containing all two-by-two combinations. Then, based on the tension anomaly features and the displacement anomaly features in each combination, a feature similarity recognition analysis is performed to obtain an adjacency matrix set reflecting the degree of feature association. Next, a convolution mapping method is used to perform feature fusion processing on all enumeration combinations under the action of the adjacency matrix to obtain a first multi-channel mapping interaction tension-displacement anomaly feature combination set. Finally, overall mean calculation is performed based on the combination set to extract the first tension-displacement anomaly feature that best represents the abnormal performance of the monitoring data under the current multi-channel.

[0050] By repeatedly performing steps a and b, all wire tension-displacement monitoring data sequences in the wire tension-displacement monitoring data sequence set are subjected to recognition analysis. Finally, the tension-displacement anomaly features corresponding to each sequence are integrated and summarized to form a complete tension-displacement anomaly feature set.

[0051] Further, the method provided by the application embodiment further comprises the following steps:

[0052] A historical wire tension-displacement anomaly monitoring data sequence is obtained. The wire anomaly interval duration and the displacement anomaly interval duration in the historical wire tension-displacement anomaly monitoring data sequence are extracted to obtain a historical wire anomaly interval duration set and a historical displacement anomaly interval duration set. The maximum value, the minimum value, and the mean value of the historical wire anomaly interval duration set and the historical displacement anomaly interval duration set are extracted respectively to construct an identification scale set. The identification channel set of the multi-channel tension-displacement anomaly feature identifier is constructed according to the identification scale set.

[0053] In the application embodiment, first, a wire tension-displacement anomaly monitoring data sequence is obtained from a historical database. The data sequence includes time series data of the corresponding relationship between tension and displacement collected during the previous tension anomaly or displacement anomaly process.

[0054] Next, the abnormal section interval extraction method is used to analyze the historical conductor tension-displacement anomaly monitoring data sequence. Specifically, for each piece of historical abnormal data in the historical conductor tension-displacement anomaly monitoring data sequence, the start and end time points of the continuous abnormal section are marked by a sliding window, and the time difference between adjacent two abnormal events is calculated, so as to extract the conductor abnormal interval length and the displacement abnormal interval length, and then the historical conductor abnormal interval length set and the historical displacement abnormal interval length set are formed.

[0055] Subsequently, the extreme value statistical method is applied to the historical conductor abnormal interval length set and the historical displacement abnormal interval length set respectively, and the maximum value, the minimum value and the mean value are extracted, so as to construct a set of representative statistical characteristic values, which constitute the recognition scale set.

[0056] Finally, according to the above recognition scale set, the recognition channel set of the multi-channel tension-displacement anomaly feature recognizer is constructed. Specifically, each scale value is taken as a time window parameter of a recognition channel, for example, channel 1 corresponds to the minimum interval scale, focusing on short-time mutation recognition; channel 2 adopts the mean interval to identify medium-frequency fluctuations; and channel 3 uses the maximum interval to capture long-term latent abnormalities. Through this channel configuration method based on statistical recognition scale, the multi-channel tension-displacement anomaly feature recognizer realizes comprehensive recognition ability of tension and displacement anomalies at different time sensitivities.

[0057] Further, the method provided by the application embodiment further comprises:

[0058] The first multi-channel enumeration combination set is enumerated and combined, and each first multi-channel enumeration combination includes two first multi-channel tension-displacement anomaly features; the first multi-channel enumeration combination set is subjected to combination internal tension anomaly feature and displacement anomaly feature similarity recognition, and an adjacency matrix set is constructed based on the recognition result; the first multi-channel enumeration combination set is subjected to convolution mapping by using the adjacency matrix set, and a first multi-channel mapping interaction tension-displacement anomaly feature combination set is obtained; and the first multi-channel mapping interaction tension-displacement anomaly feature combination set is subjected to overall mean value calculation, and the first tension-displacement anomaly feature is obtained.

[0059] In the application embodiment, first, the first multi-channel tension-displacement anomaly feature set is enumerated and combined, and any two first multi-channel tension-displacement anomaly features are selected from the first multi-channel tension-displacement anomaly feature set one by one to form a set of tension anomaly feature and displacement anomaly feature combinations, so as to generate the first multi-channel enumeration combination set. Each first multi-channel enumeration combination includes two first multi-channel tension-displacement anomaly features.

[0060] Next, the similarity of the in-combination tension anomaly features and displacement anomaly features of the first multi-channel enumeration combination set is identified, and the Euclidean distance calculation method is used to calculate the distance between the two tension anomaly features and the distance between the two displacement anomaly features in each feature combination. To ensure consistency in subsequent processing, all calculation results are normalized by minimum-maximum normalization to standardize the similarity values to the range of [0, 1], and based on the normalized results, the tension and displacement similarity values of each group are filled into a two-dimensional matrix, with the row and column corresponding to the combination index, and finally a set of adjacency matrices is formed.

[0061] Then, the first multi-channel enumeration combination set is respectively mapped by using the set of adjacency matrices. To perform this operation, a lightweight two-dimensional convolutional neural network model is constructed, which includes an input layer, a convolutional layer with a 3x3 convolutional kernel, a 2x2 max pooling layer, and a fully connected layer, and finally outputs a fixed-dimension feature vector. The training process of the model is based on the existing labeled anomaly data set, and the known tension-displacement anomaly label is used as a supervision signal, the cross-entropy loss function is used for error feedback, the Adam optimizer is used for weight update, the initial learning rate is set to 0.001, the total training rounds are set to 100 rounds, and the training batch size is set to 64. In the training process, each batch input corresponds to an adjacency matrix, and the goal is to enable the model to automatically learn the graph correlation features between the tension anomaly channel structure and the displacement anomaly channel structure, thereby improving the discriminability of the final fused features. After training, the model parameters are fixed as the backbone network of the mapping stage. Then, the trained two-dimensional convolutional neural network model is used to perform convolution operation on each tension channel adjacency matrix and displacement channel adjacency matrix in the set of adjacency matrices, respectively, to obtain a tension mapping interaction feature vector and a displacement mapping interaction feature vector, respectively, and they are paired and combined to form a first multi-channel mapping interaction tension-displacement anomaly feature combination set.

[0062] Finally, the first multi-channel mapping interaction tension-displacement anomaly feature combination set is subjected to overall mean calculation. Specifically, all tension mapping interaction feature vectors in the first multi-channel mapping interaction tension-displacement anomaly feature combination set are averaged element by element to obtain a tension anomaly fusion feature vector; and all displacement mapping interaction feature vectors are processed in the same way to obtain a displacement anomaly fusion feature vector. Subsequently, the two fusion feature vectors are combined into a unified output feature through vector splicing operation, and the feature is the first tension-displacement anomaly feature.

[0063] Further, in the method provided by the application embodiment, the method further comprises:

[0064] Synchronize and map the set of working condition data sequences to obtain a synchronized and mapped working condition data sequence chain; traverse the synchronized and mapped working condition data sequence chain to identify deviations to obtain a deviation set sequence; calculate the mean of the deviation set sequence to determine a deviation mean set, and perform data inversion based on the deviation mean set to determine the electric tension machine adjustment interlocking scale.

[0065] In the embodiment of the present application, first, the set of working condition data sequences is synchronized and mapped, that is, a time stamp alignment algorithm is used to synchronize and arrange the tension data subsequence, the rotation speed data subsequence and the output power data subsequence collected by the three electric tension machines in the set of working condition data sequences according to a uniform time step, with the sampling time recorded by the centralized control module as the reference, to obtain a synchronized and mapped working condition data sequence chain.

[0066] Then, the synchronized and mapped working condition data sequence chain is traversed, and the tension, rotation speed and power data of the three devices are identified for deviation based on the median value at each time step. Specifically, for each type of index (such as tension), the device with the most number of tension values at the median value is selected as the reference device from the tension values of the three devices, and then the tension, rotation speed and power of the device are taken as the reference to calculate the corresponding deviation of the remaining two devices at the time step. For example, at a certain time step, the tension values of the three devices are 210N, 225N and 240N, 225N is taken as the median tension value, and the device corresponding to the tension value is taken as the reference device at the time step, and the tension deviation between the other two devices and the reference device is calculated. The above operation is repeated for each time step, and finally a deviation set sequence containing all time step deviation vectors is obtained, where each item is a three-dimensional (tension, rotation speed, power) deviation combination.

[0067] Then, the entire deviation set sequence is analyzed using the dimension-by-dimension mean statistical method to calculate the full-time sequence mean of the tension deviation, the rotation speed deviation and the power deviation, respectively, to obtain a deviation mean set.

[0068] Finally, data inversion is performed based on the deviation mean set. In this process, the device selected as the reference multiple times in the deviation identification process is taken as the final fixed reference device, and the tension, rotation speed and power parameters of the device are kept unchanged, and for the remaining two devices, the values of the three dimensions in the deviation mean set are adjusted in reverse. For example, if the power deviation mean is +80W, the output power of the remaining two devices is reduced by 80W respectively; if the tension deviation is -10N, it means that the remaining devices are generally lower than the reference device, and the tension needs to be increased by 10N accordingly. Through the inversion processing, the overall consistency and cooperative coordination of the three devices in the running parameters are realized, and the electric tension machine adjustment interlocking scale is constructed with the reference device.

[0069] Further, the method provided by the embodiment of the present application further comprises:

[0070] pre-constructing a battery state anomaly feature identifier, identifying the battery container electric energy storage constraint and the battery state data sequence by using the battery state anomaly feature identifier, and obtaining the battery state anomaly feature.

[0071] In the embodiment of the present application, a battery state anomaly feature identifier is first pre-constructed. In constructing the battery state anomaly feature identifier, training data is first obtained from a preset database, the training data including input data and output data, wherein the input data includes a historical battery state data sequence and a corresponding battery container electric energy storage constraint. The output data is a battery state anomaly feature labeled by a technical expert. By using a fully connected neural network, the input data in the training data is input into the network, the loss value is calculated by comparing the output label, and the network weight is continuously adjusted by using a back propagation algorithm, so that a battery state anomaly feature identifier capable of automatically judging whether the battery state is abnormal is finally trained.

[0072] The battery container electric energy storage constraint and the battery state data sequence are input into the battery state anomaly feature identifier for identification, and the battery state anomaly feature is obtained.

[0073] Further, the method provided in the embodiment of the present application further includes the following steps:

[0074] Based on the set of tension-displacement anomaly features and the battery state anomaly feature, a set of electric tension machine adjustment parameters is determined; and the set of electric tension machine adjustment parameters is corrected according to the electric tension machine adjustment interlocking scale, so as to obtain the electric tension machine cooperative control scheme.

[0075] In the embodiment of the present application, a feature matching method is first used to compare the set of tension-displacement anomaly features with a preset tension-displacement control strategy table. The tension-displacement control strategy table records a plurality of groups of typical adjustment parameters corresponding to tension fluctuation amplitudes and displacement slopes based on historical working condition data. In the matching process, the Euclidean distance method is used as a similarity measurement standard to calculate the distance between the tension-displacement anomaly feature vector and each sample vector in the strategy table, select the closest matching item therefrom, and extract the corresponding adjustment parameters, such as an output power correction value, a speed limit value, or a response time setting. For example, when the tension anomaly amplitude is 48 N and the displacement slope is 9 mm / s, the closest matching item is a record (tension 50 N, slope 8.5 mm / s) in the strategy table, and the corresponding adjustment parameters are “reduce output power by 10% and delay response by 0.2 seconds”, which are included in the set of electric tension machine adjustment parameters.

[0076] Meanwhile, the battery state abnormality feature is matched with a preset battery load response strategy table by using a similar matching method. The battery load response strategy table is constructed based on historical abnormality features in the battery state data sequence and contains control suggestions in abnormal states such as battery overheating, sharp SOC drop, and frequent charge-discharge switching. In the matching process, the input battery state abnormality feature vector is calculated for similarity with strategy table samples, the most similar strategy record is selected, and control parameters such as suppression of charge-discharge switching frequency and reduction of discharge current upper limit are extracted. Finally, the control parameters extracted from the two types of matching results are integrated to form an initial adjustment parameter set of the electric tension machine.

[0077] Subsequently, to ensure that the coordinated action of the adjustment parameters among the three electric tension machines meets the established operation boundary, an interval constraint judgment method is used to correct the initial electric tension machine adjustment parameter set. The method takes the electric tension machine adjustment interlocking scale as the upper and lower limit standard, and checks whether each adjustment parameter falls within the interlocking scale range. For example, if the output power adjustment value obtained by the initial matching is increased by 20%, and the interlocking scale stipulates that the maximum allowed amplitude is ±15%, the value is corrected to +15%; if the speed limit value of a tension machine exceeds the corresponding interlocking interval, the upper limit or lower limit will also be truncated. Through this step, the adjustment among the three electric tension machines is ensured to be coordinated and consistent in response speed and power distribution.

[0078] Finally, the corrected adjustment parameters are used as the control basis for the operation of the electric tension machine, forming an electric tension machine coordinated control scheme.

[0079] In the embodiments of the present application, as described above, the embodiments of the present application have at least the following technical effects:

[0080] The present application connects an electric tension machine set to an electric small traction machine and a battery container through a high-voltage bus, and connects the electric tension machine set to an electric small traction machine and a centralized control module through a CAN bus network; uses a tension sensor and a displacement sensor to continuously collect the conductor status of the electric tension machine set to obtain a conductor tension-displacement monitoring data sequence set; uses the centralized control module to collect working condition data of the electric tension machine set according to preset electric tension machine working condition indicators to obtain a working condition data sequence set; continuously collects the battery status of the battery container through a battery management system to obtain a battery status data sequence; obtains basic information of the electric small traction machine, and determines the electric tension machine collaborative control scheme in combination with the conductor tension-displacement monitoring data sequence set, the working condition data sequence set and the battery status data sequence, and transmits the electric tension machine collaborative control scheme to the centralized control module to collaboratively control the electric tension machine set. The present invention solves the technical problem in the prior art that it is impossible to achieve coordinated control and dynamic energy scheduling between multiple electric tension machines and electric traction machines. Through a centralized control architecture based on the CAN bus, it integrates tension-displacement monitoring, battery status monitoring and operating condition data analysis, and generates a coordinated control solution in real time, achieving the technical effect of ensuring tension stability while optimizing energy recovery and distribution efficiency.

[0081] Embodiment 2 is based on the same inventive concept as the intelligent control method of an electric tension machine in the above embodiment. Figure 2 As shown, the present application provides an intelligent control system for an electric tension machine. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0082] The connection module 11 is used to connect the electric tension machine set and an electric small traction machine to the battery container through the high-voltage bus, and to communicate with the electric tension machine set and an electric small traction machine to the centralized control module through the CAN bus network; the status acquisition module 12 is used to use the tension sensor and the displacement sensor to continuously collect the conductor status of the electric tension machine set to obtain a conductor tension-displacement monitoring data sequence set; the working condition acquisition module 13 is used to use the centralized control module to collect the working condition data of the electric tension machine set according to the preset electric tension machine working condition indicators to obtain a working condition data sequence set; the battery status acquisition module 14 is used to continuously collect the battery status of the battery container through the battery management system to obtain a battery status data sequence; the collaborative control module 15 is used to obtain the basic information of the electric small traction machine, determine the electric tension machine collaborative control scheme in combination with the conductor tension-displacement monitoring data sequence set, the working condition data sequence set and the battery status data sequence, and transmit the electric tension machine collaborative control scheme to the centralized control module to perform collaborative control on the electric tension machine set.

[0083] Further, the system is also used to realize the following functions:

[0084] The preset electric tension machine working condition index at least includes output tension, rotating speed and output power.

[0085] Further, the system is also used to realize the following functions:

[0086] Based on the basic information of the electric traction machine, the battery container electric energy storage limit value is identified, and the battery container electric energy storage constraint is determined; the tension-displacement abnormal feature set is determined by identifying the tension-displacement abnormal features of the wire tension-displacement monitoring data sequence set; the electric tension machine adjustment interlocking scale is determined by traversing the working condition data sequence set to identify the synchronization deviation; the battery state abnormal feature is determined according to the battery container electric energy storage constraint and the battery state data sequence; the electric tension machine cooperative control scheme is determined by analyzing the tension-displacement abnormal feature set, the battery state abnormal feature and the electric tension machine adjustment interlocking scale.

[0087] Further, the system is also used to realize the following functions:

[0088] Step a: the first wire tension-displacement monitoring data sequence randomly extracted from the wire tension-displacement monitoring data sequence set is identified by using the identification channel set of the multi-channel tension-displacement abnormal feature identifier, and the first multi-channel tension-displacement abnormal feature set is determined; step b: the first multi-channel tension-displacement abnormal feature set is mapped and interacted to determine the first tension-displacement abnormal feature; steps a-b are repeated, and when the wire tension-displacement monitoring data sequence in the wire tension-displacement monitoring data sequence set is identified, the tension-displacement abnormal feature set is obtained.

[0089] Further, the system is also used to realize the following functions:

[0090] The first multi-channel tension-displacement abnormal feature set is enumerated and combined to obtain a first multi-channel enumeration combination set, wherein each first multi-channel enumeration combination includes two first multi-channel tension-displacement abnormal features; the first multi-channel enumeration combination set is identified for the similarity of the tension abnormal feature and the displacement abnormal feature within the combination, and an adjacency matrix set is constructed based on the identification result; the first multi-channel enumeration combination set is respectively convoluted and mapped by using the adjacency matrix set to obtain a first multi-channel mapping interaction tension-displacement abnormal feature combination set; the first multi-channel mapping interaction tension-displacement abnormal feature combination set is calculated for the overall mean value to obtain the first tension-displacement abnormal feature.

[0091] Further, the system is also used to implement the following functions:

[0092] Obtain a historical conductor tension-displacement anomaly monitoring data sequence; extract conductor anomaly interval duration and displacement anomaly interval duration in the historical conductor tension-displacement anomaly monitoring data sequence, obtain a historical conductor anomaly interval duration set and a historical displacement anomaly interval duration set; extract the maximum value, the minimum value and the average value of the historical conductor anomaly interval duration set and the historical displacement anomaly interval duration set respectively, and construct a recognition scale set; construct a recognition channel set of the multi-channel tension-displacement anomaly feature recognizer according to the recognition scale set.

[0093] Further, the system is also used to implement the following functions:

[0094] Synchronize and map the working condition data sequence set to obtain a synchronized mapping working condition data sequence chain; identify deviations by traversing the synchronized mapping working condition data sequence chain to obtain a deviation set sequence; calculate the average value of the deviation set sequence to determine a deviation average value set, and perform data inversion based on the deviation average value set to determine the electric tensioner adjustment interlocking scale.

[0095] Further, the system is also used to implement the following functions:

[0096] Pre-construct a battery state anomaly feature recognizer, and use the battery state anomaly feature recognizer to identify the battery container electrical energy storage constraint and the battery state data sequence to obtain the battery state anomaly feature.

[0097] Further, the system is also used to implement the following functions:

[0098] Determine an electric tensioner adjustment parameter set based on the tension-displacement anomaly feature set and the battery state anomaly feature; correct the electric tensioner adjustment parameter set according to the electric tensioner adjustment interlocking scale to obtain the electric tensioner cooperative control scheme.

[0099] It should be noted that the above sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or may be advantageous.

[0100] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0101] The specification and drawings are, of course, to be regarded in an illustrative rather than a restrictive sense. It is to be understood that any such modifications, variations, combinations or equivalents that fall within the scope of the application are intended to be embraced herein.

Claims

1. An intelligent control method for an electric tensioner, characterized by, The method comprises: The electric tensioner set and the electric small tractor are connected with the battery container through the high-voltage bus, and the electric tensioner set and the electric small tractor are connected with the centralized control module through the CAN bus network; The wire state of the electric tensioner set is continuously collected by using the tension sensor and the displacement sensor, and a wire tension-displacement monitoring data sequence set is obtained; The working condition data of the electric tensioner set are collected by using the centralized control module according to the preset electric tensioner working condition index, and a working condition data sequence set is obtained; The battery state data sequence is obtained by continuously collecting the battery state of the battery container through the battery management system; The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set; The preset electric tensioner working condition index at least includes output tension, rotating speed and output power; The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set; The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set; The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set; The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set.

2. The intelligent control method of an electric tension machine according to claim 1, wherein, The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set.

3. The intelligent control method of an electric tension machine according to claim 2, wherein, The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor is obtained, and the electric tensioner cooperative control scheme is determined in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and the electric tensioner cooperative control scheme is transmitted to the centralized control module to cooperatively control the electric tensioner set. The basic information of the electric small tractor Performing in-combination tension anomaly feature and displacement anomaly feature similarity recognition on the first multi-channel enumeration combination set, and constructing an adjacency matrix set based on the recognition result; Performing convolution mapping on the first multi-channel enumeration combination set respectively by using the adjacency matrix set, to obtain a first multi-channel mapping interactive tension-displacement anomaly feature combination set; Performing overall mean calculation on the first multi-channel mapping interactive tension-displacement anomaly feature combination set, to obtain the first tension-displacement anomaly feature.

4. The intelligent control method of an electric tension machine according to claim 2, wherein, Performing multi-channel recognition on a first wire tension-displacement monitoring data sequence randomly extracted from the wire tension-displacement monitoring data sequence set by using the multi-channel tension-displacement anomaly feature recognizer, to determine a first multi-channel tension-displacement anomaly feature set, including: Obtaining historical wire tension-displacement anomaly monitoring data sequences; Extracting wire anomaly interval time lengths and displacement anomaly interval time lengths from the historical wire tension-displacement anomaly monitoring data sequences, to obtain a historical wire anomaly interval time length set and a historical displacement anomaly interval time length set; Respectively extracting maximum values, minimum values and mean values of the historical wire anomaly interval time length set and the historical displacement anomaly interval time length set, to construct an identification scale set; Constructing an identification channel set of the multi-channel tension-displacement anomaly feature recognizer according to the identification scale set.

5. The intelligent control method of an electric tension machine according to claim 1, wherein, Performing synchronous deviation recognition on the working condition data sequence set by iteration, to determine an electric tensioner adjustment interlock scale, including: Performing synchronous mapping association on the working condition data sequence set, to obtain a synchronous mapping working condition data sequence chain; Performing deviation recognition on the synchronous mapping working condition data sequence chain by iteration, to obtain a deviation set sequence; Calculating a mean value of the deviation set sequence, to determine a deviation mean value set, and performing data inversion based on the deviation mean value set, to determine the electric tensioner adjustment interlock scale.

6. The intelligent control method of an electric tension machine according to claim 1, wherein, Pre-constructing a battery state anomaly feature recognizer, and recognizing the battery container energy storage constraint and the battery state data sequence by using the battery state anomaly feature recognizer, to obtain the battery state anomaly feature.

7. The intelligent control method of an electric tension machine according to claim 1, wherein, Performing analysis according to the tension-displacement anomaly feature set, the battery state anomaly feature and the electric tensioner adjustment interlock scale, to determine an electric tensioner cooperative control scheme, including: Determining an electric tensioner adjustment parameter set based on the tension-displacement anomaly feature set and the battery state anomaly feature; Correcting the electric tensioner adjustment parameter set according to the electric tensioner adjustment interlock scale, to obtain the electric tensioner cooperative control scheme.

8. An intelligent control system for an electric tension machine, characterized in that: The system is used to perform the electric tensioner intelligent control method according to any one of claims 1-7, and the system includes: A connection module, configured to connect the electric tensioner set and an electric small traction machine to the battery container through a high-voltage bus, and to communicate the electric tensioner set and the electric small traction machine with a centralized control module through a CAN bus network; A state acquisition module, configured to continuously acquire wire states of the electric tensioner set by using tension sensors and displacement sensors, to obtain a wire tension-displacement monitoring data sequence set; A state acquisition module, configured to continuously acquire wire states of the electric tensioner set by using tension sensors and displacement sensors, to obtain a wire tension-displacement monitoring data sequence set; The working condition acquisition module is configured to acquire working condition data sequences of the electric tensioner set by using the centralized control module according to preset electric tensioner working condition indexes. The battery state acquisition module is configured to continuously acquire battery state data of the battery container by using a battery management system. The cooperative control module is configured to acquire basic information of the electric traction machine, determine an electric tensioner cooperative control scheme in combination with the wire tension-displacement monitoring data sequence set, the working condition data sequence set and the battery state data sequence, and transmit the electric tensioner cooperative control scheme to the centralized control module to cooperatively control the electric tensioner set.

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