Communication data processing method and device of inspection trolley, terminal equipment and storage medium

Abstract

The invention discloses a communication data processing method and device of an inspection trolley, terminal equipment and a storage medium, and belongs to the technical field of communication, and the method comprises the steps: obtaining current moment state data and current moment estimation state data of the inspection trolley; substituting the state data at the current moment into a nonlinear model to obtain a simulation model of the inspection trolley; performing Taylor expansion on the simulation model to obtain a linearization matrix and boundary data; determining a matrix inequality according to the linearization matrix and the boundary data, and solving the matrix inequality to obtain a gain matrix; and performing state analysis according to the gain matrix and the estimated state data at the current moment to obtain the estimated state data at the next moment of the inspection trolley, so that the problem of low inspection efficiency caused by the fact that the fault of the inspection trolley is not estimated in advance can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a communication data processing method and device of a patrol trolley, a terminal equipment and a storage medium. BACKGROUND

[0002] With the development of Internet of Vehicles and unmanned driving technology, network communication plays an important role in unmanned vehicle system. The unmanned vehicle is usually used as a patrol trolley to perform system patrol, such as underground cable patrol. However, the data collected by the current patrol trolley is mainly the environmental data of the environment and the current running state, and the future running state of the patrol trolley cannot be estimated. Therefore, when the patrol trolley fails, the patrol task progress will be affected, resulting in low patrol efficiency. SUMMARY

[0003] The present application provides a communication data processing method and device of a patrol trolley, a terminal equipment and a storage medium, which can solve the problem of low patrol efficiency caused by not estimating the failure of the patrol trolley in advance.

[0004] The communication data processing method of the patrol trolley provided by the present application comprises: obtaining current time state data and current time estimated state data of the patrol trolley; wherein the acquisition of the initial state data comprises: based on the last time state data, performing state estimation on the current time to obtain the last time estimated state data; substituting the current time state data into a nonlinear model to obtain a simulation model of the patrol trolley; performing Taylor expansion on the simulation model to obtain a linearization matrix and boundary data; determining a matrix inequality according to the linearization matrix and the boundary data, solving the matrix inequality to obtain a gain matrix; performing state analysis according to the gain matrix and the current time estimated state data to obtain next time predicted state data of the patrol trolley.

[0005] Further, the Taylor expansion of the simulation model to obtain a linearization matrix, a remainder boundary and a boundary matrix comprises: determining an expression corresponding to the simulation model based on the simulation model; performing Taylor expansion on the expression by a Taylor expansion method with a remainder based on the current time state data and the last time estimated state data to obtain a Taylor expansion formula; solving a linearization matrix, a remainder boundary and a boundary matrix based on the Taylor expansion formula.

[0006] Further, the acquisition of the nonlinear model comprises: obtaining a kinematic model of the inspection trolley; matrixing the kinematic model to obtain a nonlinear model.

[0007] Further, after the next time prediction state data of the inspection trolley is obtained, the method further comprises: determining a current time full-symmetrical polytope estimation range based on the current time estimation state data; judging whether the current time state data is in the current time full-symmetrical polytope estimation range; if yes, calculating the next time estimation state data of the inspection trolley according to the current time full-symmetrical polytope estimation range; if no, not doing any operation.

[0008] Further, after the next time prediction state data of the inspection trolley is obtained, the method further comprises: obtaining a known state set of the inspection trolley; wherein the known state set comprises an attacked state and an unattacked state; judging whether the previous time prediction state data intersects with the known state set; if yes, the inspection trolley is attacked; if no, the inspection trolley is not attacked.

[0009] Further, the state analysis based on the gain matrix and the current time estimation state data to obtain the next time prediction state data of the inspection trolley comprises: substituting the gain matrix, the current time estimation state data, the linearization matrix, the residual boundary and the boundary matrix into a state prediction formula to obtain the next time prediction state data of the inspection trolley; wherein the next time prediction state data comprises a next time state prediction value and a next time full-symmetrical polytope prediction range; the state prediction formula comprises: ; ; ; ; In the formula, is the state prediction value at k+1 time, represents the center point at , the generating matrix is the full-symmetrical polytope prediction range, i.e. the prediction state data at k+1 time; is the generating matrix of the disturbance set . the first order term coefficient of Taylor expansion; and respectively the center point and the generating matrix of the zonotope estimation range at time k; and are the lower bound and the upper bound of , , is the high order residual term.

[0010] Further, the calculating the next time estimation state data of the inspection vehicle according to the current time zonotope estimation range comprises: determining a zonotope prediction range at the next time according to the next time prediction state data; substituting the zonotope prediction range at the next time and the current time zonotope estimation range into a state estimation formula to obtain the next time estimation state data of the inspection vehicle; wherein the next time estimation state data comprises a next time state estimation value and a next time zonotope estimation range; and the state estimation formula comprises: ; ; ; wherein, is a state estimation value at time k+1, denotes that the center point is at , the generating matrix is the next time zonotope estimation range of , , and respectively the position and the azimuth angle of the vehicle at time k in the x direction, x in the y direction; y , , , is a front wheel steering angle, and are the distances from the center of gravity to the front and rear axles; , , is a three-dimensional unit matrix, is a measurement output, is a measurement noise; , denotes that the center point is at the origin, and the generating matrix is the zonotope noise data; is an observer gain matrix;​ is a generating matrix of a full-symmetry polytope prediction range at k+1 moment.

[0011] Another embodiment of the present application also provides a communication data processing device of the inspection trolley, comprising a data acquisition module, a simulation model module, a Taylor expansion module, a data calculation module and a result generation module. The data acquisition module is configured to acquire current moment state data and previous moment estimated state data of the inspection trolley, wherein the initial state data acquisition comprises: performing state estimation on the current moment based on the previous moment state data to obtain the previous moment estimated state data. The simulation model module is configured to substitute the current moment state data into a nonlinear model to obtain a simulation model of the inspection trolley. The Taylor expansion module is configured to perform Taylor expansion on the simulation model to obtain a linearization matrix and boundary data. The data calculation module is configured to determine a matrix inequality according to the linearization matrix and the boundary data, solve the matrix inequality, and obtain a gain matrix. The result generation module is configured to perform state analysis according to the gain matrix and the previous moment estimated state data to obtain the current moment estimated state data of the inspection trolley.

[0012] Another embodiment of the present application also provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, when the processor executes the computer program, the steps of the communication data processing method of the inspection trolley provided by the present application are implemented.

[0013] Another embodiment of the present application also provides a computer readable storage medium item, comprising a stored computer program, when the computer program runs, the device where the computer readable storage medium is located executes the steps of the communication data processing method of the inspection trolley provided by the present application.

[0014] The present application has the following beneficial effects: The application discloses a communication data processing method of a patrol trolley. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some of the embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative effort based on these drawings also belong to the protection scope of the present application.

[0016] Figure 1 FIG. 1 is a flowchart of a communication data processing method of a patrol trolley according to an embodiment of the present application; Figure 2 FIG. 2 is a structural diagram of a communication data processing device of a patrol trolley according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort also belong to the protection scope of the present application.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit the present application; the terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover non-exclusive inclusion.

[0019] In the description of the embodiments of the present application, the technical terms "first", "second" and the like are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise explicitly and specifically limited.

[0020] In this paper, the reference to "embodiments" means that the specific features, structures or properties described in conjunction with the embodiments can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment to other embodiments. The skilled in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.

[0021] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0022] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two), and similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0023] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connection", "connection", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanical connection, or it can be electrical connection; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0024] Reference Figure 1 To solve the problem of low inspection efficiency caused by not estimating the fault of the inspection trolley in advance, an embodiment of the present application provides a communication data processing method of an inspection trolley, comprising: 101, obtaining the current time state data and the current time estimated state data of the inspection trolley; wherein the acquisition of the initial state data comprises: based on the last time state data, the state estimation of the current time is obtained, and the last time estimated state data is obtained.

[0025] 102, substituting the current moment state data into the nonlinear model to obtain a simulation model of the inspection trolley.

[0026] 103, Taylor expanding the simulation model to obtain a linearization matrix and boundary data.

[0027] In the embodiment, the Taylor expansion of the simulation model to obtain a linearization matrix, a remainder boundary and a boundary matrix comprises: Based on the simulation model, an expression corresponding to the simulation model is determined; According to the current moment state data and the last moment estimated state data, the expression is Taylor expanded by a Taylor expansion method with a remainder to obtain a Taylor expansion formula; Based on the Taylor expansion formula, a linearization matrix, a remainder boundary and a boundary matrix are solved.

[0028] In the embodiment, the obtaining of the nonlinear model comprises: Obtaining a kinematic model of the inspection trolley; Matrixing the kinematic model to obtain a nonlinear model.

[0029] In a specific embodiment, the present application considers the following kinematic model of an unmanned vehicle: (1) wherein, , and are respectively k the position and orientation angle of the vehicle in the x direction, y direction at the moment, V is the vehicle speed, , and represent system uncertainty and disturbance, is the front wheel steering angle, and are the distances from the center of gravity to the front and rear axles.

[0030] Define , , then the above formula can be expressed as a nonlinear model: (2) wherein, .

[0031] Define the measurement output as , and , then (3) wherein, , is a three-dimensional identity matrix, is a measurement output, is a measurement noise.

[0032] In the existing state estimation and control of unmanned vehicles, it is usually assumed that the statistical characteristics of system disturbances and noises are known, for example, satisfying Gaussian distribution, and the mean and variance are known. However, in actual situations, the statistical characteristics of disturbances and noises are unknown, and it is possible to only know the upper and lower bounds. In order to deal with the state estimation problem of unmanned vehicles in this case, the present application assumes that the initial state of the underground cable inspection vehicle disturbances and noises , satisfy the following conditions: , , (4) wherein, denotes a zonotope with the center point at and the generating matrix ; similarly, and denote zonotopes with the center point at the origin and the generating matrices and , respectively. It should be noted that assumption (4) can better reflect the actual situation, and only the measurement error range of the sensor needs to be known, without the need for related statistical information.

[0033] In a specific embodiment, it is assumed that the data collected by the underground cable inspection vehicle is sent to the control center through a wireless network, and remote state estimation is performed at the control center. In this case, the data sent by the underground cable inspection vehicle is vulnerable to malicious attacks by competitors, such as denial of service attacks, replay attacks, and false data injection attacks, which further makes it impossible for the control center to obtain accurate underground cable inspection vehicle state information. The present application considers that the underground cable inspection vehicle is subjected to the following false data injection attack: (5) wherein, is the false data injected by the competitor, is a diagonal matrix used to describe which measurement output channel is attacked, i.e. , , If , it means that the i th output channel is attacked, otherwise it is not attacked.

[0034] In a specific embodiment, it is noted that the underground cable inspection trolley system model (2) is a nonlinear model. The present invention uses the Taylor expansion method with remainder terms to linearize it as follows: (6) in, for The estimated value, For higher-order remainder terms, Higher-order remainders The following conditions must be met: (7) Higher-order remainder Further, it can be expressed in the following form: (8) in, and for The lower and upper bounds can be obtained by solving the following optimization problem: (9) (10) in, , , , , , For matrix The number of columns, For matrix No. i Line 1 j The elements of the column.

[0035] 104. Based on the linearized matrix and the boundary data, determine the matrix inequalities, solve the matrix inequalities, and obtain the gain matrix.

[0036] matrix It can be calculated by solving the following matrix inequality: in, , and P Let be the matrix to be found and P It is a positive definite symmetric matrix. This represents the transpose of the symmetric element of the matrix. Solving the above inequality yields... and P Then, calculations can be performed to obtain .

[0037] 105. Perform state analysis based on the gain matrix and the estimated state data at the current time to obtain the predicted state data of the inspection vehicle at the next time.

[0038] In this embodiment, after obtaining the predicted state data of the inspection vehicle at the next moment, the method further includes: Based on the estimated state data at the current moment, determine the estimation range of the fully symmetric multicell at the current moment; Determine whether the current state data is within the range of the full symmetric multicell estimation at the current time; If so, then calculate the estimated state data of the inspection vehicle at the next moment based on the estimated range of the fully symmetric polytope at the current moment; If not, no action will be taken.

[0039] In this embodiment, after obtaining the predicted state data of the inspection vehicle at the next moment, the method further includes: Obtain the known state set of the inspection vehicle; wherein, the known state set includes: attacked state and unattacked state; Determine whether the predicted state data from the previous time step intersects with the known state set; If so, the inspection vehicle has been attacked; If not, then the inspection vehicle was not attacked.

[0040] In this embodiment, the step of performing state analysis based on the gain matrix and the estimated state data at the current moment to obtain the predicted state data of the inspection vehicle at the next moment includes: Substituting the gain matrix, the estimated state data at the current time, the linearization matrix, the remainder boundary, and the boundary matrix into the state prediction formula, the predicted state data of the inspection vehicle at the next time is obtained; wherein, the predicted state data at the next time includes: the predicted state value at the next time and the predicted range of the fully symmetric polytope at the next time; the state prediction formula includes: ; ; ; ; In the formula, This is the predicted state value at time k+1. Indicates the center point is at The generating matrix is The full symmetric multicell prediction range, i.e. the prediction state data at time k+1; For interference set The generating matrix; The coefficients of the first-order terms in the Taylor expansion; and Let be the center point and the generating matrix of the full symmetric polytope estimation range at time k, respectively; and for The lower and upper bounds, , , This is a higher-order remainder term.

[0041] In one specific embodiment, , .

[0042] In this embodiment, calculating the estimated state data of the inspection vehicle at the next moment based on the current time-to-time fully symmetric polytope estimation range includes: Based on the predicted state data for the next moment, determine the prediction range of the fully symmetric multicell for the next moment; Substituting the predicted range of the fully symmetric polytope at the next moment and the estimated range of the fully symmetric polytope at the current moment into the state estimation formula, the estimated state data of the inspection vehicle at the next moment is obtained; wherein, the estimated state data at the next moment includes: the estimated state value at the next moment and the estimated range of the fully symmetric polytope at the next moment; the state estimation formula includes: ; ; ; In the formula, This is the state estimate at time k+1. Indicates the center point is at The generating matrix is The estimated range of the fully symmetric multicell at the next time step, i.e., the estimated state data at time k+1; , , and They are respectively k Vehicles at all times x direction, y The position and azimuth of the direction; , , For the front wheel steering angle, and This is the distance from the center of gravity to the front and rear axles; , , It is a three-dimensional identity matrix. For measurement output, For measuring noise; , represents that the center point is at the origin, and the generating matrix is a zonotope noise data; is an observer gain matrix; is a generating matrix of the zonotope prediction range at k+1 moment.

[0043] In a specific embodiment, whether the sensor data is attacked, i.e. whether the one-step prediction set of the underground cable inspection trolley state and the known state set containing the output information at the current moment are from the intersection, is judged by using the intersection theory of sets. The specific attack detector detection rule is designed as follows: If , the system is attacked, and if , the system is not attacked, wherein represents an empty set, .

[0044] In actual application, it is usually assumed that the matrix is a diagonal matrix, i.e. . Therefore, in the attack detection rule designed above, the set is the intersection of the known state sets, i.e. , , is the i-th row of the matrix C , i is the i-th element of the output vector . i Case one: if or

[0045] , the attack detection rate can reach 100%. Wherein, is the pseudo-inverse of the matrix , , , , .

[0046] Case two: if , the attack detector cannot detect the attack.

[0047] As shown in Figure 2 , on the basis of the above method embodiment, a corresponding device embodiment is provided; An embodiment of the present application provides a communication data processing device of an inspection trolley, comprising: a data acquisition module 201, a simulation model module 202, a Taylor expansion module 203, a data calculation module 204 and a result generation module 205.​​ The data acquisition module is configured to acquire current time state data and previous time estimated state data of the inspection trolley; wherein the acquisition of the initial state data comprises: performing state estimation on the current time based on the previous time state data to obtain the previous time estimated state data; The simulation model module is configured to substitute the current time state data into a nonlinear model to obtain a simulation model of the inspection trolley; The Taylor expansion module is configured to perform Taylor expansion on the simulation model to obtain a linearization matrix and boundary data; The data calculation module is configured to determine a matrix inequality according to the linearization matrix and the boundary data, and solve the matrix inequality to obtain a gain matrix; The result generation module is configured to perform state analysis according to the gain matrix and the previous time estimated state data to obtain current time predicted state data of the inspection trolley.

[0048] It can be understood that the above device item embodiments correspond to the method item embodiments of the present application, and can realize the communication data processing method of the inspection trolley provided by any one of the above method item embodiments.

[0049] It should be noted that the device embodiments described above are only schematic, and part or all of the modules can be selected to achieve the purpose of the present embodiment. In addition, in the device embodiment provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement without creative labor.

[0050] On the basis of the above-mentioned embodiment of the communication data processing method of the inspection trolley, another embodiment of the present application provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to realize the communication data processing method of the inspection trolley of any one of the embodiments of the present application.

[0051] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device.

[0052] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The terminal device can include, but is not limited to, a processor and a memory.

[0053] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like. The processor is a control center of the terminal device, and is connected to various parts of the terminal device through various interfaces and lines.

[0054] On the basis of the above-mentioned method embodiment, another embodiment of the present application provides a computer readable storage medium, including a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located performs the communication data processing method of the inspection trolley according to any one of the above-mentioned method embodiments of the present application.

[0055] The modules / units integrated in the device / terminal device can be stored in a computer readable storage medium if they are realized in the form of software function units and sold or used as independent products. Based on this understanding, all or part of the processes in the above-mentioned embodiments can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of each method embodiment can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0056] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements are also considered to be within the scope of the present application.

Claims

1. A communication data processing method for an inspection vehicle, characterized in that, include: The current state data and estimated state data of the inspection vehicle are obtained; wherein, the acquisition of the initial state data includes: estimating the state of the current time based on the state data of the previous time to obtain the estimated state data of the previous time. Substitute the current state data into the nonlinear model to obtain the simulation model of the inspection vehicle; The simulation model is subjected to Taylor expansion to obtain the linearized matrix and boundary data; Based on the linearized matrix and the boundary data, a matrix inequality is determined, and the matrix inequality is solved to obtain the gain matrix. Based on the gain matrix and the estimated state data at the current moment, state analysis is performed to obtain the predicted state data of the inspection vehicle at the next moment.

2. The communication data processing method for the inspection vehicle as described in claim 1, characterized in that, The step of performing a Taylor expansion on the simulation model to obtain the linearized matrix, the remainder term boundary, and the boundary matrix includes: Based on the simulation model, determine the expression corresponding to the simulation model; Based on the current state data and the estimated state data from the previous time, the expression is expanded using the Taylor expansion method with remainder terms to obtain the Taylor expansion formula. Based on the Taylor expansion formula, the linearized matrix, the remainder term boundary, and the boundary matrix are solved.

3. The communication data processing method for the inspection vehicle as described in claim 2, characterized in that, The acquisition of the nonlinear model includes: Obtain the kinematic model of the inspection vehicle; The kinematic model is matrixed to obtain a nonlinear model.

4. The communication data processing method for the inspection vehicle as described in claim 3, characterized in that, After obtaining the predicted state data of the inspection vehicle at the next moment, the method further includes: Based on the estimated state data at the current moment, determine the estimation range of the fully symmetric multicell at the current moment; Determine whether the current state data is within the range of the full symmetric multicell estimation at the current time; If so, then calculate the estimated state data of the inspection vehicle at the next moment based on the estimated range of the fully symmetric polytope at the current moment; If not, no action will be taken.

5. The communication data processing method for the inspection vehicle as described in claim 4, characterized in that, After obtaining the predicted state data of the inspection vehicle at the next moment, the method further includes: Obtain the known state set of the inspection vehicle; wherein, the known state set includes: attacked state and unattacked state; Determine whether the predicted state data from the previous time step intersects with the known state set; If so, the inspection vehicle has been attacked; If not, then the inspection vehicle was not attacked.

6. The communication data processing method for the inspection vehicle as described in claim 5, characterized in that, The step of performing state analysis based on the gain matrix and the estimated state data at the current moment to obtain the predicted state data of the inspection vehicle at the next moment includes: Substituting the gain matrix, the estimated state data at the current time, the linearization matrix, the remainder boundary, and the boundary matrix into the state prediction formula, the predicted state data of the inspection vehicle at the next time is obtained; wherein, the predicted state data at the next time includes: the predicted state value at the next time and the predicted range of the fully symmetric polytope at the next time; the state prediction formula includes: ； ； ； ； In the formula, This is the predicted state value at time k+1. Indicates the center point is at The generating matrix is The full symmetric multicell prediction range, i.e. the prediction state data at time k+1; For interference set The generating matrix; The coefficients of the first-order terms in the Taylor expansion; and Let be the center point and the generating matrix of the full symmetric polytope estimation range at time k, respectively; and for The lower and upper bounds, , , This is a higher-order remainder term.

7. The communication data processing method for the inspection vehicle as described in claim 6, characterized in that, The step of calculating the estimated state data of the inspection vehicle at the next moment based on the current fully symmetric multicell estimation range includes: Based on the predicted state data for the next moment, determine the prediction range of the fully symmetric multicell for the next moment; Substituting the predicted range of the fully symmetric polytope at the next moment and the estimated range of the fully symmetric polytope at the current moment into the state estimation formula, the estimated state data of the inspection vehicle at the next moment is obtained; wherein, the estimated state data at the next moment includes: the estimated state value at the next moment and the estimated range of the fully symmetric polytope at the next moment; the state estimation formula includes: ； ； ； In the formula, This is the state estimate at time k+1. Indicates the center point is at The generating matrix is The estimated range of the fully symmetric multicell at the next time step, i.e., the estimated state data at time k+1; , , and They are respectively k Vehicles at all times x direction, y The position and azimuth of the direction; , , For the front wheel steering angle, and This is the distance from the center of gravity to the front and rear axles; , , It is a three-dimensional identity matrix. For measurement output, For measuring noise; , The center point is at the origin, and the generating matrix is... Fully symmetric multicellular noise data; The observer gain matrix; is the generating matrix for the prediction range of the fully symmetric multicell at time k+1.

8. A communication data processing device for an inspection vehicle, characterized in that, include: The system includes a data acquisition module, a simulation model module, a Taylor expansion module, a data calculation module, and a result generation module. The data acquisition module is used to acquire the current state data and the estimated state data of the previous moment of the inspection vehicle; wherein, the acquisition of the initial state data includes: estimating the state of the current moment based on the state data of the previous moment to obtain the estimated state data of the previous moment. The simulation model module is used to substitute the current state data into a nonlinear model to obtain a simulation model of the inspection vehicle. The Taylor expansion module is used to perform Taylor expansion on the simulation model to obtain a linearized matrix and boundary data; The data calculation module is used to determine matrix inequalities based on the linearized matrix and the boundary data, solve the matrix inequalities, and obtain the gain matrix. The result generation module is used to perform state analysis based on the gain matrix and the estimated state data at the current time to obtain the predicted state data of the inspection vehicle at the next time.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the communication data processing method for the inspection vehicle as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the communication data processing method of the inspection vehicle as described in any one of claims 1-7.

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