A method for state perception and life cycle health assessment of distribution network equipment

Abstract

The present invention relates to the field of power transmission and transformation technology, and in particular to a method for state perception and full life cycle health assessment of distribution network equipment. The method comprises: obtaining relevant quantity data of a transformer; constructing a thermochemical degradation characteristic vector and a wet electrical degradation characteristic vector, and normalizing each component thereof; constructing expressions of an internal affine surface and an external linear surface of the transformer respectively, calculating a directed normalized distance from the normalized thermochemical degradation characteristic vector to the internal affine surface of the transformer, and simultaneously calculating a directed normalized distance from the normalized wet electrical degradation characteristic vector to the external linear surface of the transformer, and judging whether the current state of the transformer is on the risk side, calculating a risk vector and outputting the risk vector; introducing a non-isotropic Markov tensor metric to calculate the coupling strength of the thermochemical degradation and wet electrical degradation of the transformer, calculating the transformer health index and determining the health state, and judging the transformer state as qualified when the transformer health index reaches a preset health threshold.

Description

Technical Field

[0001] The present invention relates to the field of power transmission and transformation technology, and in particular to a method for state perception and full life cycle health assessment of distribution network equipment. Background Art

[0002] Transformers are core equipment in power systems, and their operating status directly impacts the safety and stability of the power grid. Over long-term operation, transformers can gradually degrade due to internal thermochemical degradation (such as insulation aging caused by excessively high winding hot spots and elevated carbon monoxide concentrations in the oil) and external hydroelectric degradation (such as insulation dampness and partial discharge caused by ambient humidity). Failure to promptly assess these degradations can lead to sudden failures and significant economic losses.

[0003] Shortcomings of existing technologies: Internal thermochemical degradation (e.g., rising temperature) and external hydroelectric degradation (e.g., increased humidity) do not occur independently but rather interact in a synergistic manner (e.g., high temperature accelerates humidity-induced insulation breakdown). However, existing technologies often employ an "independent assessment + simple weighting" model (e.g., calculating the temperature degradation index and humidity degradation index separately and then adding them in a fixed ratio). This completely ignores the mutual influence of the two types of degradation, resulting in an assessment that fails to reflect "coupling risk." Furthermore, transformer degradation is a dynamic process (e.g., load factor fluctuations within 24 hours can cause dynamic temperature changes). Existing technologies often employ "instantaneous value assessment" or "simple average calculation," failing to consider the statistical characteristics of the monitored data (e.g., fluctuation amplitude and trend correlation). Summary of the Invention

[0004] The main purpose of the present invention is to provide a method for state perception and full life cycle health assessment of distribution network equipment, which clearly characterizes the linkage relationship between internal thermochemical degradation and external hydroelectric degradation through the "covariance coupling matrix"; compared with the independent assessment of the existing technology, this scheme incorporates "independent risk" and "coupled risk" into the same assessment framework, so that the health index is closer to the actual degradation law of the equipment; a dual-threshold system is constructed through the "internal affine surface" and "external linear surface": the positive and negative values ​​of the directed distance vector are used to distinguish between the "safe side", "critical state" and "risk side"; the absolute value of the directed distance can quantify the degree of deviation, solving the rough problem of the "black or white" traditional single threshold.

[0005] The technical solutions of the present invention are as follows:

[0006] First, a method for state perception and life cycle health assessment of distribution network equipment is proposed. The method includes the following steps:

[0007] S1. Obtain relevant quantity data of the transformer, wherein the relevant quantity data includes winding hot spot temperature, load factor, carbon monoxide concentration, water content in oil, number of partial discharge pulses, and relative humidity;

[0008] S2. Based on the relevant quantity data of the transformer, a thermochemical degradation characteristic vector and a wet electrical degradation characteristic vector are constructed, and the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data are obtained. Based on the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, each component in the thermochemical degradation characteristic vector and the wet electrical degradation characteristic vector is normalized, and a normalized thermochemical degradation characteristic vector and a normalized wet electrical degradation characteristic vector are output;

[0009] S3. Construct expressions for the transformer's internal affine surface and external linear surface, respectively. Calculate the directed normalized distance between the normalized vector of the thermochemical degradation characteristic and the transformer's internal affine surface. Simultaneously, calculate the directed normalized distance between the normalized vector of the wet electrical degradation characteristic and the transformer's external linear surface. Determine whether the transformer is currently at risk, calculate the risk vector, and output it.

[0010] S4. Introduce the anisotropic Markov tensor metric to calculate the coupling strength of the transformer's thermochemical degradation and wet-electric degradation, calculate the transformer health index and determine the health status. When the transformer health index reaches the preset health threshold, the transformer is considered to be in a qualified state.

[0011] A further improvement of the present invention is that S2 comprises the following specific steps:

[0012] S21, constructing a thermochemical degradation feature vector and a wet electrical degradation feature vector, wherein the thermochemical degradation feature vector is expressed as The wet electrical degradation characteristic vector is expressed as ;in, is the winding hot spot temperature, is the load rate, is the carbon monoxide concentration, is the water content in the oil, is the number of partial discharge pulses, is the relative humidity;

[0013] S22, respectively obtain the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, and based on the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, calculate the thermochemical degradation characteristic vector and the characteristic vector of wet electrical degradation Normalize the components in the equation and output the normalized thermochemical degradation characteristic vector and the normalized vector of wet electrical degradation characteristics .

[0014] A further improvement of the present invention is that the calculation formula for the normalization process in S22 is:

[0015] ;

[0016] ;

[0017] in, is the i-th component in the normalized vector of thermochemical degradation characteristics, is the i-th component in the thermochemical degradation eigenvector, is the industry reference lower limit value of the i-th component in the thermochemical degradation characteristic vector, is the industry reference upper limit value of the i-th component in the thermochemical degradation characteristic vector, is the i-th component in the normalized vector of wet electrical degradation characteristics, is the i-th component in the wet electrical degradation eigenvector, is the industry reference lower limit value of the i-th component in the wet electrical degradation characteristic vector, It is the industry reference upper limit value of the i-th component in the wet electrical degradation characteristic vector, and the value of i ranges from 1 to 3.

[0018] A further improvement of the present invention is that S3 includes the following specific steps:

[0019] S31. Construct an expression of the internal affine surface of the transformer, where the expression of the internal affine surface of the transformer is: ;in, , ; Construct an expression for the external linear surface of the transformer, the expression for the external linear surface of the transformer is: ;in, , ;

[0020] S32. Calculate the directed normalized distance from the normalized thermochemical degradation characteristic vector to the internal affine surface of the transformer. The calculation formula is: At the same time, the directed normalized distance from the normalized vector of the wet electrical degradation characteristic to the external linear surface of the transformer is calculated. The calculation formula is: ; express The model, express Model.

[0021] A further improvement of the present invention is that S3 further includes:

[0022] S33, determine whether the current state of the transformer is at risk. When the current state of the transformer is judged to be on the safe side of the internal affine surface, When , it is judged that the current state of the transformer is at the critical position between the safe side and the risk side of the internal affine surface; otherwise, it is judged that the current state of the transformer is at the risk side of the internal affine surface; when When the transformer is currently in the safe side of the external linear plane, When , it is judged that the current state of the transformer is at the critical position between the safe side and the risk side of the external linear surface; otherwise, it is judged that the current state of the transformer is at the risk side of the external linear surface;

[0023] S34. Calculate and output the risk vector. The calculation formula of the risk vector is: ;in, ; .

[0024] A further improvement of the present invention is that S4 includes the following specific steps:

[0025] S41, extracting N consecutive valid acquisition time points at 5-minute intervals, where the valid acquisition time points are the time points when the transformer is simultaneously on the safe side of the internal affine surface and the external linear surface, i.e. and , get the directed normalized distance vector pair of N consecutive valid acquisition time points , where k is 1-N and N is 288;

[0026] S42, through the formula: ; Calculate the covariance coupling matrix ; When the determinant of the covariance coupling matrix A satisfies or When Set to 0 and fine-tune the covariance coupling matrix. The fine-tuning formula is: ;in, is the fine-tuned covariance coupling matrix, For the general The covariance coupling matrix after being set to 0, I is the identity matrix.

[0027] A further improvement of the present invention is that the step S4 further comprises:

[0028] S43. Extract the covariance coupling matrix after fine-tuning and risk vector , calculate the Mahalanobis coupling distance, and the calculation formula of the coupling distance is: ;

[0029] S44. Calculate the transformer health index using the following formula: ;in, The preset health threshold.

[0030] On the second aspect, a computer-readable storage medium is proposed, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned distribution network equipment status perception and full life cycle health assessment method is implemented.

[0031] On the third aspect, an electronic device is proposed, comprising a memory for storing instructions; and a processor for executing the instructions, so that the device implements the above-mentioned method for state perception and full life cycle health assessment of distribution network equipment.

[0032] The technical effects of the present invention are as follows:

[0033] A method for state perception and full life cycle health assessment of distribution network equipment was constructed. The linkage between internal thermochemical degradation and external hydroelectric degradation was clearly characterized through the "covariance coupling matrix". Compared with the independent assessment of existing technologies, this scheme incorporates "independent risk" and "coupled risk" into the same assessment framework, making the health index closer to the actual degradation law of the equipment. A dual-threshold system was constructed through "internal affine surface" and "external linear surface": the positive and negative values ​​of the directed distance vector are used to distinguish between the "safe side", "critical state" and "risk side". The absolute value of the directed distance can quantify the degree of deviation, solving the rough "black or white" problem of the traditional single threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0035] Figure 1 This is a flow chart of a method for state perception and life cycle health assessment of distribution network equipment according to embodiment 1 of the present invention. DETAILED DESCRIPTION

[0036] Example 1

[0037] This embodiment proposes a method for state perception and full life cycle health assessment of distribution network equipment. It uses a "covariance coupling matrix" to clearly characterize the linkage between internal thermochemical degradation and external hydroelectric degradation. Compared with the independent assessment of existing technologies, this solution incorporates "independent risk" and "coupled risk" into the same assessment framework, making the health index closer to the actual degradation law of the equipment. It constructs a dual-threshold system through an "internal affine surface" and an "external linear surface": the positive and negative values ​​of the directed distance vector are used to distinguish between the "safe side", "critical state" and "risk side". The absolute value of the directed distance can quantify the degree of deviation, solving the "black or white" roughness problem of the traditional single threshold.

[0038] Specifically, such as Figure 1 As shown, the method for state perception and life cycle health assessment of distribution network equipment proposed in this embodiment includes the following specific steps:

[0039] S1. Obtain relevant quantity data of the transformer, wherein the relevant quantity data includes winding hot spot temperature, load factor, carbon monoxide concentration, water content in oil, number of partial discharge pulses, and relative humidity;

[0040] S2. Based on the relevant quantity data of the transformer, a thermochemical degradation characteristic vector and a wet electrical degradation characteristic vector are constructed, and the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data are obtained. Based on the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, each component in the thermochemical degradation characteristic vector and the wet electrical degradation characteristic vector is normalized, and a normalized thermochemical degradation characteristic vector and a normalized wet electrical degradation characteristic vector are output;

[0041] S3. Construct expressions for the transformer's internal affine surface and external linear surface, respectively. Calculate the directed normalized distance between the normalized vector of the thermochemical degradation characteristic and the transformer's internal affine surface. Simultaneously, calculate the directed normalized distance between the normalized vector of the wet electrical degradation characteristic and the transformer's external linear surface. Determine whether the transformer is currently at risk, calculate the risk vector, and output it.

[0042] S4. Introduce the anisotropic Markov tensor metric to calculate the coupling strength of the transformer's thermochemical degradation and wet-electric degradation, calculate the transformer health index and determine the health status. When the transformer health index reaches the preset health threshold, the transformer is considered to be in a qualified state.

[0043] In this embodiment, S2 includes the following specific steps:

[0044] S21, constructing a thermochemical degradation feature vector and a wet electrical degradation feature vector, wherein the thermochemical degradation feature vector is expressed as ; The wet electrical degradation characteristic vector is expressed as ;in, is the winding hot spot temperature, is the load rate, is the carbon monoxide concentration, is the water content in the oil, is the number of partial discharge pulses, is the relative humidity;

[0045] S22, respectively obtain the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, and based on the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, calculate the thermochemical degradation characteristic vector and the characteristic vector of wet electrical degradation Normalize the components in the equation and output the normalized thermochemical degradation characteristic vector and the normalized vector of wet electrical degradation characteristics .

[0046] In this embodiment, the calculation formula for the normalization process in S22 is:

[0047] ;

[0048] ;

[0049] in, is the i-th component in the normalized vector of thermochemical degradation characteristics, is the i-th component in the thermochemical degradation eigenvector, is the industry reference lower limit value of the i-th component in the thermochemical degradation characteristic vector, is the industry reference upper limit value of the i-th component in the thermochemical degradation characteristic vector, is the i-th component in the normalized vector of wet electrical degradation characteristics, is the i-th component in the wet electrical degradation eigenvector, is the industry reference lower limit value of the i-th component in the wet electrical degradation characteristic vector, It is the industry reference upper limit value of the i-th component in the wet electrical degradation characteristic vector, and the value of i ranges from 1 to 3.

[0050] In this embodiment, S3 includes the following specific steps:

[0051] S31. Construct an expression of the internal affine surface of the transformer, where the expression of the internal affine surface of the transformer is: ;in, , ; Construct an expression for the external linear surface of the transformer, the expression for the external linear surface of the transformer is: ;in, , ;

[0052] S32. Calculate the directed normalized distance from the normalized thermochemical degradation characteristic vector to the internal affine surface of the transformer. The calculation formula is: At the same time, the directed normalized distance from the normalized vector of the wet electrical degradation characteristic to the external linear surface of the transformer is calculated. The calculation formula is: ; express The model, express Model.

[0053] In this embodiment, S3 further includes:

[0054] S33, determine whether the current state of the transformer is at risk. When the current state of the transformer is judged to be on the safe side of the internal affine surface, When , it is judged that the current state of the transformer is at the critical position between the safe side and the risk side of the internal affine surface; otherwise, it is judged that the current state of the transformer is at the risk side of the internal affine surface; when When the transformer is currently in the safe side of the external linear plane, When , it is judged that the current state of the transformer is at the critical position between the safe side and the risk side of the external linear surface; otherwise, it is judged that the current state of the transformer is at the risk side of the external linear surface;

[0055] S34. Calculate and output the risk vector. The calculation formula of the risk vector is: ;in, ; .

[0056] In this embodiment, S4 includes the following specific steps:

[0057] S41, extracting N consecutive valid acquisition time points at 5-minute intervals, where the valid acquisition time points are the time points when the transformer is simultaneously on the safe side of the internal affine surface and the external linear surface, i.e. and , get the directed normalized distance vector pair of N consecutive valid acquisition time points , where k is 1-N and N is 288;

[0058] S42, through the formula: ; Calculate the covariance coupling matrix ; When the determinant of the covariance coupling matrix A satisfies or When Set to 0 and fine-tune the covariance coupling matrix. The fine-tuning formula is: ;in, is the fine-tuned covariance coupling matrix, For the general The covariance coupling matrix after being set to 0, I is the identity matrix.

[0059] In this embodiment, the S4 further includes:

[0060] S43. Extract the covariance coupling matrix after fine-tuning and risk vector , calculate the Mahalanobis coupling distance, and the calculation formula of the coupling distance is: ;

[0061] S44. Calculate the transformer health index using the following formula: ;in, The preset health threshold.

[0062] Example 2

[0063] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned distribution network equipment status perception and full life cycle health assessment method by calling the computer program stored in the memory.

[0064] This electronic device may vary significantly due to configuration or performance, and may include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the method for state perception and lifecycle health assessment of distribution network equipment provided in the above-mentioned method embodiment. The electronic device may also include other components for implementing device functions. For example, the electronic device may also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0065] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented as a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0066] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0067] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowcharts and block diagrams, as well as combinations of processes and blocks in the flowcharts or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 A process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 A process or multiple processes and boxes Figure 1 The steps for the function specified in one or more boxes.

[0069] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A method for state perception and life cycle health assessment of distribution network equipment, characterized by: The specific steps include: S1. Obtain relevant quantity data of the transformer, wherein the relevant quantity data includes winding hot spot temperature, load factor, carbon monoxide concentration, water content in oil, number of partial discharge pulses, and relative humidity; S2. Based on the relevant quantity data of the transformer, a thermochemical degradation characteristic vector and a wet electrical degradation characteristic vector are constructed, and the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data are obtained. Based on the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, each component in the thermochemical degradation characteristic vector and the wet electrical degradation characteristic vector is normalized, and a normalized thermochemical degradation characteristic vector and a normalized wet electrical degradation characteristic vector are output; S3. Construct expressions for the transformer's internal affine surface and external linear surface, respectively. Calculate the directed normalized distance between the normalized vector of the thermochemical degradation characteristic and the transformer's internal affine surface. Simultaneously, calculate the directed normalized distance between the normalized vector of the wet electrical degradation characteristic and the transformer's external linear surface. Determine whether the transformer is currently at risk, calculate the risk vector, and output it. S4. Introduce the non-isotropic Markov tensor metric to calculate the coupling strength between the thermochemical degradation and the wet-electric degradation of the transformer, calculate the transformer health index and determine the health status. When the transformer health index reaches the preset health threshold, the transformer is judged to be in a qualified state. The S2 includes the following specific steps: S21, constructing a thermochemical degradation feature vector and a wet electrical degradation feature vector, wherein the thermochemical degradation feature vector is expressed as The wet electrical degradation characteristic vector is expressed as ;in, is the winding hot spot temperature, is the load rate, is the carbon monoxide concentration, is the water content in the oil, is the number of partial discharge pulses, is the relative humidity; S22, respectively obtain the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, and based on the industry reference upper limit value and the industry reference lower limit value of each relevant quantity data, calculate the thermochemical degradation characteristic vector and the characteristic vector of wet electrical degradation Normalize the components in the equation and output the normalized thermochemical degradation characteristic vector and the normalized vector of wet electrical degradation characteristics ; The S3 includes the following specific steps: S31. Construct an expression of the internal affine surface of the transformer, where the expression of the internal affine surface of the transformer is: ;in, , ; Construct an expression for the external linear surface of the transformer, the expression for the external linear surface of the transformer is: ;in, , ; S32. Calculate the directed normalized distance from the normalized thermochemical degradation characteristic vector to the internal affine surface of the transformer. The calculation formula is: At the same time, the directed normalized distance from the normalized vector of the wet electrical degradation characteristic to the external linear surface of the transformer is calculated. The calculation formula is: ; express The model, express The model; Said S3 further comprises: S33, determine whether the current state of the transformer is at risk. When the current state of the transformer is judged to be on the safe side of the internal affine surface, When , it is judged that the current state of the transformer is at the critical position between the safe side and the risk side of the internal affine surface; otherwise, it is judged that the current state of the transformer is at the risk side of the internal affine surface; when When the transformer is currently in the safe side of the external linear plane, When , it is judged that the current state of the transformer is at the critical position between the safe side and the risk side of the external linear surface; otherwise, it is judged that the current state of the transformer is at the risk side of the external linear surface; S34. Calculate and output the risk vector. The calculation formula of the risk vector is: ;in, ; ; The S4 includes the following specific steps: S41, extracting N consecutive valid acquisition time points at 5-minute intervals, where the valid acquisition time points are the time points when the transformer is simultaneously on the safe side of the internal affine surface and the external linear surface, i.e. and , get the directed normalized distance vector pair of N consecutive valid acquisition time points , where k is 1-N and N is 288; S42, through the formula: ; Calculate the covariance coupling matrix ; When the determinant of the covariance coupling matrix A satisfies or When Set to 0 and fine-tune the covariance coupling matrix. The fine-tuning formula is: ;in, is the fine-tuned covariance coupling matrix, For the general The covariance coupling matrix after being set to 0, I is the identity matrix; Said S4 further comprises: S43. Extract the covariance coupling matrix after fine-tuning and risk vector , calculate the Mahalanobis coupling distance, and the calculation formula of the coupling distance is: ; S44. Calculate the transformer health index using the following formula: ;in, is the preset health threshold.

2. A method for state perception and life cycle health assessment of distribution network equipment according to claim 1, characterized in that: The calculation formula for the normalization process in S22 is: ； ； in, is the i-th component in the normalized vector of thermochemical degradation characteristics, is the i-th component in the thermochemical degradation eigenvector, is the industry reference lower limit value of the i-th component in the thermochemical degradation characteristic vector, is the industry reference upper limit value of the i-th component in the thermochemical degradation characteristic vector, is the i-th component in the normalized vector of wet electrical degradation characteristics, is the i-th component in the wet electrical degradation eigenvector, is the industry reference lower limit value of the i-th component in the wet electrical degradation characteristic vector, It is the industry reference upper limit value of the i-th component in the wet electrical degradation characteristic vector, and the value of i ranges from 1 to 3.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements a distribution network equipment state perception and full life cycle health assessment method as described in any one of claims 1-2.

4. An electronic device, characterized in that: It includes a memory for storing instructions; a processor for executing the instructions, so that the device implements a distribution network equipment status perception and full life cycle health assessment method as described in any one of claims 1 to 2.

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