A method and device for monitoring and evaluating a power grid engineering material supply chain

Abstract

The application provides a power grid engineering material supply chain monitoring and evaluation method and device, comprising: obtaining first monitoring data of each power grid equipment being transported and stored at a transportation and storage node; determining whether a target power grid equipment is abnormal according to internal electrical data of the target power grid equipment for each power grid equipment; analyzing the comprehensive influence of external environmental factors on the target abnormal power grid equipment according to all external environmental data of the target abnormal power grid equipment to obtain an environmental comprehensive influence quantitative index for each abnormal power grid equipment; obtaining second monitoring data of each normal power grid equipment being installed and debugged at an installation and debugging node; determining a corresponding installation and debugging risk evaluation index according to the second monitoring data of the target normal power grid equipment for each normal power grid equipment; and evaluating a safety risk value of the power grid engineering material supply chain according to all environmental comprehensive influence quantitative indexes and all installation and debugging maximum risk evaluation indexes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain management, and particularly relates to a method and device for monitoring and evaluating a power grid engineering material supply chain. BACKGROUND

[0002] Power grid engineering material supply chain management refers to, in the process of power engineering construction and operation and maintenance, through reasonable planning, procurement, transportation, storage and distribution of various materials, to ensure the smooth implementation and continuous operation of the power grid engineering. The management involves efficient deployment and control of equipment, materials, tools and other materials, thereby optimizing the overall efficiency and cost control of the supply chain, reducing inventory backlog, and ensuring that various materials arrive at the construction site on time and as needed. Effective supply chain management not only improves the progress and quality of power grid engineering construction, but also enhances the stability and security of the power system, especially in the face of emergencies or natural disasters, to ensure rapid recovery and maintenance.

[0003] The traditional static evaluation method of the power grid engineering material supply chain usually relies on pre-set standards and regular checks, mainly through manual data collection and manual analysis to evaluate the status of power grid equipment and material supply chain. The defect of this method is the lack of real-time and flexibility, which cannot reflect the actual state change of equipment or materials in time, especially in the face of complex power grid environment and unexpected situations, which easily leads to delayed response or inaccurate evaluation. In addition, static evaluation cannot dynamically monitor the impact of changes in external environment on power grid safety, leading to the inability to timely discover potential risks or hidden dangers, making it difficult to achieve accurate early warning and timely intervention, thereby affecting the reliability and safety of the power grid. Therefore, a method is needed to improve the accuracy of monitoring and evaluation of the power grid engineering material supply chain. SUMMARY

[0004] The present application provides a method and device for monitoring and evaluating a power grid engineering material supply chain, which can improve the accuracy of monitoring and evaluation of the power grid engineering material supply chain.

[0005] In a first aspect of the present application, a method for monitoring and evaluating a power grid engineering material supply chain is provided, the power grid engineering material supply chain comprising: transportation and storage nodes and installation and debugging nodes, the monitoring and evaluation method comprising:

[0006] At the transportation and storage nodes, first monitoring data of each power grid equipment being transported and stored is obtained, the first monitoring data comprising: internal electrical data and external environmental data of the power grid equipment in a non-running state;

[0007] For each power grid equipment, it is determined whether the target power grid equipment has an abnormality according to the internal electrical data of the target power grid equipment, and the target power grid equipment determined to have an abnormality is confirmed as an abnormal power grid equipment;

[0008] For each abnormal power grid device, according to all external environment data of the target abnormal power grid device, the comprehensive influence of external environment factors on the target abnormal power grid device is analyzed to obtain an environment comprehensive influence quantitative index;

[0009] In the installation and debugging node, second monitoring data of each normal power grid device being installed and debugged is obtained, the second monitoring data including internal electrical data and external environment data of the normal power grid device in a running state;

[0010] For each normal power grid device, an installation and debugging risk assessment index corresponding to the target normal power grid device is determined according to the second monitoring data of the target normal power grid device;

[0011] According to the environment comprehensive influence quantitative index of all abnormal power grid devices and the installation and debugging maximum risk assessment index of all normal power grid devices, a safety risk value of the entire power grid engineering material supply chain is evaluated.

[0012] In some embodiments, the step of determining whether the target power grid device is abnormal according to the internal electrical data of the target power grid device includes:

[0013] A normal internal electrical parameter reference data set corresponding to the target power grid device is obtained, the normal internal electrical parameter reference data set recording reference values of each internal electrical parameter of the target power grid device in a non-running state;

[0014] The collected internal electrical data of the target power grid device is matched with the reference values corresponding to the normal internal electrical parameter reference data set of the target power grid device, and a matching degree is obtained based on a regression algorithm;

[0015] The matching degree is used to determine whether the target power grid device is abnormal.

[0016] In some embodiments, the step of matching the collected internal electrical data of the target power grid device with the reference values corresponding to the normal internal electrical parameter reference data set of the target power grid device, and obtaining a matching degree based on a regression algorithm includes:

[0017] The matching degree is determined based on the following formula:

[0018]

[0019] Wherein, ε is the matching degree, n is the total number of internal electrical data of the target power grid device, x i is the i-th internal electrical data of the target power grid device, s i is the reference value of the electrical parameter corresponding to the i-th internal electrical data of the target power grid device, and w idenoted as the weight of the electrical parameter corresponding to the i-th internal electrical data of the target power grid equipment, and p is the penalty degree of the control error and takes a positive integer value.

[0020] In some embodiments, the step of analyzing the comprehensive impact of external environmental factors on the target abnormal power grid equipment based on all external environmental data of the target abnormal power grid equipment, and obtaining a quantitative index of comprehensive environmental impact, includes:

[0021] For each external environmental data point of the target abnormal power grid equipment, a preset environmental data-influence mapping algorithm corresponding to the target external environmental data is used to determine the influence measure corresponding to the target external environmental data.

[0022] The comprehensive environmental impact quantification index is calculated based on the impact measures corresponding to each external environmental data.

[0023] In some embodiments, the step of calculating the comprehensive environmental impact quantification index based on the impact measures corresponding to each external environmental data includes:

[0024] The comprehensive environmental impact quantification index is determined based on the following formula:

[0025]

[0026] Where H is the comprehensive environmental impact quantification index, m is the total number of external environmental data for the target abnormal power grid equipment, and W i The preset impact weight of the external environmental factors corresponding to the i-th external environmental data on the power grid equipment. i Let be the impact measure corresponding to the i-th external environment data, and let impact_max be the maximum value among the m impact measures corresponding to all m external environment data.

[0027] In some embodiments, the step of determining the corresponding installation and commissioning risk assessment index based on the second monitoring data of the target normal power grid equipment includes:

[0028] The installation and commissioning risk assessment indicators are determined based on the following formula:

[0029]

[0030] Where Q represents the safety commissioning risk assessment indicator. This is a vector composed of all internal electrical data and external environmental data in the second monitoring data arranged in a preset order. This refers to the pre-configured operating status reference vector for the target normal power grid equipment. Dimensions and The dimensions are the same, and sim() represents the similarity function.

[0031] In some embodiments, the step of evaluating the safety risk value of the entire power grid engineering material supply chain according to the environmental comprehensive influence quantification index of all abnormal power grid equipment and the installation and debugging maximum risk evaluation index of all normal power grid equipment comprises:

[0032] The safety risk value P1 of the transportation and storage node is evaluated according to the environmental comprehensive influence quantification index corresponding to each abnormal power grid equipment;

[0033] The safety risk value P2 of the installation and debugging node is evaluated according to the installation and debugging maximum risk evaluation index corresponding to each normal power grid equipment;

[0034] The safety risk value P0 of the entire power grid engineering material supply chain is evaluated according to the safety risk value P1 and the safety risk value P2, and P0 is positively correlated with at least one of P1 and P2.

[0035] The safety risk value P1 of the transportation and storage node is:

[0036]

[0037] Wherein, A represents the total number of abnormal power grid equipment in the transportation and storage node, H a represents the environmental comprehensive influence quantification index of the a-th abnormal power grid equipment, γ a represents the preset importance degree weight coefficient configured for the a-th abnormal power grid equipment;

[0038] The safety risk value P2 of the installation and debugging node is:

[0039]

[0040] Wherein, B represents the total number of normal power grid equipment in the installation and debugging node, Q b represents the installation and debugging maximum risk evaluation index of the b-th normal power grid equipment, μ b represents the preset importance degree weight coefficient configured for the b-th normal power grid equipment.

[0041] In a second aspect of the present application, a power grid engineering material supply chain monitoring and evaluation device is provided, which is used to implement the monitoring and evaluation method provided in the first aspect, and comprises:

[0042] A first acquisition module is configured to acquire first monitoring data of each power grid equipment transported and stored at the transportation and storage node, wherein the first monitoring data comprises internal electrical data and external environmental data of the power grid equipment in a non-running state;

[0043] a judging module configured to judge, for each power grid device, whether the target power grid device has an abnormality according to internal electrical data of the target power grid device, and confirm the target power grid device that is judged to have an abnormality as an abnormal power grid device;

[0044] a first processing module configured to, for each abnormal power grid device, analyze a comprehensive influence of external environmental factors on the target abnormal power grid device according to all external environmental data of the target abnormal power grid device, and obtain an environmental comprehensive influence quantification index;

[0045] a second obtaining module configured to, in the installation and debugging node, obtain second monitoring data of each normal power grid device that is installed and debugged, the second monitoring data including internal electrical data and external environmental data of the normal power grid device in a running state;

[0046] a second processing module configured to, for each normal power grid device, determine a corresponding installation and debugging risk assessment index according to the second monitoring data of the target normal power grid device;

[0047] an assessment module configured to assess a security risk value of the entire power grid engineering material supply chain according to the environmental comprehensive influence quantification index of all abnormal power grid devices and the installation and debugging maximum risk assessment index of all normal power grid devices.

[0048] In a third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the monitoring and assessment method according to any one of the above aspects.

[0049] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores instructions, when the instructions are executed, the monitoring and assessment method according to any one of the above aspects is performed.

[0050] The disclosure provides a power grid engineering material supply chain monitoring and evaluation method and device, wherein first monitoring data of each power grid equipment being transported and stored is obtained at a transportation and storage node, for each power grid equipment, whether the target power grid equipment is abnormal is determined according to internal electrical data of the target power grid equipment, and the target power grid equipment determined to be abnormal is confirmed as an abnormal power grid equipment, then for each abnormal power grid equipment, a comprehensive environmental impact quantitative index is obtained according to all external environmental data of the target abnormal power grid equipment, the comprehensive environmental impact quantitative index is obtained according to all external environmental data of the target abnormal power grid equipment, the comprehensive influence of external environmental factors on the target abnormal power grid equipment is analyzed, and the second monitoring data of each normal power grid equipment being installed and debugged is obtained at an installation and debugging node, and for each normal power grid equipment, the installation and debugging risk evaluation index corresponding to the target normal power grid equipment is determined according to the second monitoring data of the target normal power grid equipment, and finally, the safety risk value of the entire power grid engineering material supply chain is evaluated according to the comprehensive environmental impact quantitative index of all abnormal power grid equipments and the maximum installation and debugging risk evaluation index of all normal power grid equipments. The technical scheme of the disclosure can realize dynamic monitoring of the safety risk value of the power grid engineering material supply chain, and the monitoring personnel can discover and respond to the safety risk in the supply chain in time and effectively. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is a flowchart of a power grid engineering material supply chain monitoring and evaluation method disclosed by the embodiment of the present application;

[0052] Figure 2 is a module schematic diagram of a power grid engineering material supply chain monitoring and evaluation device disclosed by the embodiment of the present application;

[0053] Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiment of the present application.

[0054] Mark explanation: 201, first acquisition module; 202, judgment module; 203, first processing module; 204, second acquisition; 205, second processing module; 206, evaluation module module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0055] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be described clearly and completely in conjunction with the drawings in the embodiment of the specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments.

[0056] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to indicate an example, an illustration or an illustration. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concept in a specific manner.

[0057] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first", "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the indicated technical features. Therefore, the features defined with "first", "second" can be explicitly or implicitly included one or more features. The terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0058] The power grid engineering material supply chain management aims to ensure the smooth implementation and continuous operation of power grid engineering by reasonably planning, purchasing, transporting, storing and distributing various materials. However, the traditional static evaluation method relies on manual collection and analysis of data, lacks real-time and flexibility, and cannot accurately reflect the actual state changes of equipment or materials, and it is also difficult to respond to sudden situations and external environmental impacts on power grid safety. Therefore, it is urgent to develop a more accurate dynamic monitoring and evaluation method to improve the efficiency of the supply chain and the safety of the power grid system.

[0059] The monitoring and evaluation method for the power grid engineering material supply chain disclosed in the embodiments of the present application can be applied to a server, which includes but is not limited to electronic devices such as mobile phones, tablet computers, wearable devices, PC (Personal Computer), and can also be a background server running a monitoring and evaluation method for a power grid engineering material supply chain. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0060] In the present disclosure, the entire power grid engineering material supply chain includes at least two important nodes: transportation and storage nodes and installation and debugging nodes; wherein the transportation and storage nodes refer to the nodes that transport the purchased power grid equipment to the corresponding final destination and store them, in which process multiple transportation and multiple storage of the power grid equipment may be required. The installation and debugging nodes refer to the nodes that install and debug the power grid equipment at the corresponding final destination.

[0061] The present embodiment discloses a monitoring and evaluation method for a power grid engineering material supply chain, referring to Figure 1comprising the following steps S110-S160:

[0062] S110, at the transportation and storage node, obtaining first monitoring data of each power grid equipment being transported and stored, the first monitoring data comprising internal electrical data and external environmental data of the power grid equipment in a non-operation state.

[0063] It should be noted that the power grid equipment is in a non-operation state during transportation and storage.

[0064] The internal electrical data of the power grid equipment refers to a series of physical quantities used to describe and characterize the electrical performance, operation characteristics and working conditions of the power grid equipment; the internal electrical data is of great significance to the design, selection, operation and maintenance of the power grid equipment, as well as the planning and analysis of the power grid. In the present disclosure, the electrical parameters can include voltage, current, resistance, etc. at certain key nodes inside the power grid equipment.

[0065] The external environmental data of the power grid equipment refers to relevant data used to describe the surrounding environment of the power grid equipment. For example, the humidity, temperature, electric field intensity, magnetic field intensity, vibration (such as vibration of the box carrying the power grid equipment, thereby causing the power grid equipment to vibrate) of the surrounding environment of the power grid equipment, etc.

[0066] In step S110, various types of intelligent sensors and Internet of Things devices (such as voltage, current, resistance, temperature, humidity, electric field intensity, magnetic field intensity, vibration sensors) can be arranged in corresponding positions, for example, directly arranged at key positions on the power grid equipment or key positions around the power grid equipment; these intelligent sensors and Internet of Things devices can collect the internal electrical data and external environmental data of each power grid equipment in real time, and these data can be transmitted in real time to a central data platform or cloud through wireless communication technologies such as Wi-Fi, Zigbee, 5G, etc. for storage and preliminary processing. In order to ensure the real-time and accuracy of the data, the sensor data of each node will be continuously monitored, and the information from different sensors will be integrated through data fusion technology, so as to comprehensively reflect the internal electrical state and external environmental state of the power grid equipment. In this way, the changes of the internal electrical data and external environmental data of the power grid equipment can be continuously tracked in the transportation and storage node, so as to ensure that potential risks and abnormalities can be discovered in time.

[0067] In step S110, the first monitoring data of a single power grid equipment collected at any time includes one or more different internal electrical data and one or more external environmental data. The number and type of internal electrical data included in the first monitoring data of different power grid equipment can be the same or different; the number and type of external environmental data included in the first monitoring data of different power grid equipment can be the same or different, which is not limited in the present disclosure.

[0068] In the transportation and storage node, the power grid equipment being transported and stored is in a non-operating state, and thus it is impossible to determine whether the power grid equipment is abnormal through the working condition of the power grid equipment. Therefore, in the present disclosure, the internal electrical data of the power grid equipment is collected and analyzed in real time to determine whether the power grid equipment is abnormal in real time. In addition, in the transportation and storage node, external environmental factors are one of the most important reasons for the abnormality of the power grid equipment. Therefore, by analyzing the influence of external environmental factors on abnormal power grid equipment, the safety risk of the transportation and storage node can be effectively analyzed, thereby providing a data basis for the final analysis of the overall safety risk of the power grid engineering material supply chain. The related content will be described in detail later.

[0069] S120, for each power grid equipment, determining whether the target power grid equipment is abnormal according to the internal electrical data of the target power grid equipment, and confirming the target power grid equipment determined to be abnormal as an abnormal power grid equipment.

[0070] The internal electrical data of the target power grid equipment can effectively reflect whether the target power grid equipment is abnormal. As an example, in a normal non-operating state, the voltage at a certain key node in the target power grid equipment should be about 1V (for example, the normal safety range is 0.8V-1.2V). When it is monitored that the voltage at the key node is 0V or 2V (out of the normal safety range), it can be reflected to a certain extent that the target power grid equipment may be abnormal. As another example, in a normal non-operating state, the resistance between two key nodes in the target power grid equipment should be 10KΩ. When it is monitored that the voltage at the key node is 50Ω (a serious deviation), it can be reflected to a certain extent that the target power grid equipment may be abnormal.

[0071] In the present disclosure, whether the target power grid equipment is abnormal can be determined according to the internal electrical data of the target power grid equipment based on the following schemes. The following schemes are only some optional embodiments of the present disclosure, which will not limit the technical solutions of the present disclosure.

[0072] Scheme one, for each type of power grid equipment, a corresponding "normal internal electrical parameter range comparison table" is designed. The normal internal electrical parameter range comparison table records the normal value range of each internal electrical parameter of the type of power grid equipment in the non-operating state. At this time, in step S120, the normal internal electrical parameter range comparison table corresponding to the target power grid equipment can be obtained first, and then each internal electrical data collected from the target power grid equipment is compared with the corresponding normal value range recorded in the normal internal electrical parameter range comparison table corresponding to the target power grid equipment to detect whether each internal electrical data of the target power grid equipment is within the corresponding normal value range. Then, whether the target power grid equipment is abnormal is determined according to the detection result.

[0073] As an example, when the detection result is that all internal electrical data of the target power grid device is within the corresponding normal value range, it can be judged that the target power grid device is normal; when the detection result is that at least one internal electrical data of the target power grid device is outside the normal value range, it can be judged that the target power grid device is abnormal.

[0074] Of course, considering the sensor data error factor, it can also be designed that: when the ratio of the number of internal electrical data of the target power grid device within the corresponding normal value range to the number of all internal electrical data corresponding to the target power grid device is greater than or equal to a preset proportion threshold, it is judged that the target power grid device is normal; when the ratio of the number of internal electrical data of the target power grid device within the corresponding normal value range to the number of all internal electrical data corresponding to the target power grid device is less than the preset proportion threshold, it is judged that the target power grid device is abnormal.

[0075] Scheme two, for various types of power grid devices, corresponding abnormal detection models can be configured respectively, the input of the abnormal detection model is a vector composed of all internal electrical parameters of the power grid device in the non-running state in a preset order, and the output of the abnormal detection model is the detection result of whether the power grid device is abnormal. Specifically, taking the abnormal detection model of a certain type of power grid device as an example, the internal electrical data vectors of some normal power grid devices of this type can be obtained as positive samples, and the internal electrical data vectors of some abnormal power grid devices of this type can be obtained as negative samples, and then the positive and negative samples are used to train the abnormal detection model of this type of power grid device to obtain the trained abnormal detection model. At this time, in step S120, only the abnormal detection model corresponding to the target power grid device needs to be obtained, and then the collected all internal electrical data (essentially a vector) of the target power grid device is input into the corresponding abnormal detection model, and the corresponding abnormal detection model outputs the result of whether the target power grid device is abnormal.

[0076] Scheme three, for various types of power grid devices, corresponding normal internal electrical parameter reference data sets are designed respectively, and the normal internal electrical parameter reference data set records the reference values of each internal electrical parameter of the power grid device in the non-running state. At this time, in step S120, the normal internal electrical parameter reference data set corresponding to the target power grid device can be obtained first, and then the collected all internal electrical data of the target power grid device is matched with the corresponding reference values in the normal internal electrical parameter reference data set of the target power grid device to obtain the matching degree, and then whether the target power grid device is abnormal is judged according to the matching degree.

[0077] As an optional solution for calculating the matching degree of the total internal electrical data of the target power grid equipment and the corresponding normal internal electrical parameter reference data set, the total internal electrical data of the target power grid equipment can be arranged according to a preset parameter arrangement order (for example, the first bit represents the reference value of the current at the first node, the second bit represents the reference value of the voltage at the second node, the third bit represents the reference value of the resistance between the third node and the fourth node, and so on) to form a first vector, and the data in the normal internal electrical parameter reference data of the target power grid equipment is arranged according to the same preset parameter arrangement order to form a second vector; the matching degree is determined according to the first vector and the second vector. Alternatively, the similarity (for example, the cosine similarity) of the first vector and the second vector can be used as the matching degree.

[0078] As another optional solution for calculating the matching degree of the total internal electrical data of the target power grid equipment and the corresponding normal internal electrical parameter reference data set, a regression algorithm is used to calculate the matching degree between the total internal electrical data of the target power grid equipment and the corresponding normal internal electrical parameter reference data set. The regression model calculates the difference between each internal electrical data and the reference value of the corresponding electrical parameter, and determines the final matching degree value based on the difference. The formula can be realized by the regression calculation formula as described above, and is specifically represented as:

[0079]

[0080] wherein ε is the matching degree, n is the total number of internal electrical data of the target power grid equipment, x i is the i-th internal electrical data of the target power grid equipment, s i is the reference value of the electrical parameter corresponding to the i-th internal electrical data of the target power grid equipment, w i is the weight of the electrical parameter corresponding to the i-th internal electrical data of the target power grid equipment, and p is the penalty degree of the error and is a positive integer. When p = 1, the influence of the matching degree calculation on the error is linear, that is, the matching degree decreases linearly with the increase of the deviation, which is suitable for the case that the error is not sensitive to the penalty; when p = 2, the influence of the matching degree calculation on the error is quadratic nonlinearity, that is, the greater the deviation, the faster the matching degree decreases, which is suitable for the case that the error is sensitive, such as the safety evaluation of key parameters such as voltage and current; when p = 3 or more, the influence of the matching degree calculation on the error becomes extremely sensitive, that is, when the deviation increases slightly, the matching degree decreases rapidly, which is suitable for electrical equipment with extremely high safety requirements and strict control of deviation (such as high-voltage switches, transformers, etc.). In actual application, the specific value of p can be adjusted according to actual needs.

[0081] In the formula, in order to measure the difference between the actual internal electrical data and the reference value of the corresponding electrical parameter, the absolute difference |x i-s i | represents the absolute deviation between the actual value and the standard value. The absolute difference reflects the degree to which internal electrical data deviates from the baseline value of the corresponding electrical parameter. To make the difference measure comparable across different internal electrical data scales, the formula |x| is used. i -s i | and the reference value s of the corresponding electrical parameters i Dividing by this yields a standardized measure of difference. This standardization process eliminates the influence of internal electrical data units and dimensions, ensuring that differences in voltage, current, and other internal electrical data can be compared under the same standard. In this formula, w i This refers to the pre-assigned weights of the electrical parameters corresponding to each internal electrical data point. Different electrical parameters may have different levels of importance in power grid security. By introducing weighting factors, the contribution of different electrical parameters to the overall matching degree calculation can be adjusted according to their degree of influence. For example, voltage fluctuations may be more important than current fluctuations, so voltage parameters are given a higher weight, thus making their impact on the final matching degree greater. By adjusting the degree of control error penalty, the nonlinear effects of errors can be controlled. If the control error penalty is large, the penalty for the matching degree will be more significant when the difference between the internal electrical data and the baseline value of the corresponding electrical parameter is large. This nonlinear penalty helps to more sensitively capture the impact of large deviations on security, thereby improving the response capability in practical applications.

[0082] The core principle of this formula is to calculate the standardized difference between internal electrical data and the benchmark values ​​of corresponding electrical parameters, and then combine this with a weighting factor and an error penalty mechanism to derive a matching degree value to quantify the degree of matching between all internal electrical data and the corresponding normal internal electrical parameter benchmark dataset. In this way, it is possible to assess in real time whether any abnormalities exist in the power grid equipment, thereby providing a basis for safety management and risk warning.

[0083] The greater the standardized difference between each internal electrical data point and the baseline value of its corresponding electrical parameter, the smaller the calculated matching degree value, and the higher the probability that the target power grid equipment is abnormal. Based on this, a matching degree threshold can be preset in this disclosure. When the matching degree calculated by the formula is greater than or equal to the preset matching degree threshold, it is determined that the target power grid equipment is not abnormal; conversely, when the matching degree calculated by the formula is less than the preset matching degree threshold, it is determined that the target power grid equipment is abnormal.

[0084] S130, for each abnormal power grid device, based on all external environmental data of the target abnormal power grid device, analyze the comprehensive impact of external environmental factors on the target abnormal power grid device, and obtain the comprehensive environmental impact quantification index.

[0085] As described above, in the transportation and storage nodes, external environmental factors are one of the most important reasons for the abnormality of power grid equipment. Therefore, by analyzing the influence of external environmental factors on abnormal power grid equipment, the safety risk of the transportation and storage nodes can be effectively analyzed.

[0086] Specifically, in the present disclosure, each type of power grid equipment is configured with a corresponding set of environmental data-influence quantity mapping algorithms, which records a plurality of environmental data-influence quantity mapping algorithms corresponding to different environmental factors. The environmental data-influence quantity mapping algorithm is used to map the corresponding external environmental data to the corresponding influence quantity, and the influence quantity represents the quantification of the negative influence of the corresponding external environmental data on the target abnormal power grid equipment. The preset environmental data-influence quantity mapping algorithm corresponding to each environmental factor in the set of environmental data-influence quantity mapping algorithms corresponding to each type of power grid equipment can be designed in advance according to the pre-collected historical data, which is not limited in the present disclosure.

[0087] As an example, A1 type power grid equipment corresponds to three different environmental factors: temperature, humidity, and electric field intensity. At this time, the set of environmental data-influence quantity mapping algorithms configured for A1 type power grid equipment includes three different environmental data-influence quantity mapping algorithms: a first temperature data-influence quantity mapping algorithm, a first humidity data-influence quantity mapping algorithm, and a first electric field intensity-influence quantity mapping algorithm. B1 type power grid equipment corresponds to three different environmental factors: temperature, humidity, and magnetic field intensity. At this time, the set of environmental data-influence quantity mapping algorithms configured for B1 type power grid equipment includes three different environmental data-influence quantity mapping algorithms: a second temperature data-influence quantity mapping algorithm, a second humidity data-influence quantity mapping algorithm, and a first magnetic field intensity-influence quantity mapping algorithm.

[0088] Since the same environmental factor may have the same or different influence on different types of power grid equipment, the environmental data-influence quantity mapping algorithm configured for the same environmental factor by different types of power grid equipment can be the same or different. As an example, A1 type power grid equipment is more sensitive to temperature differences, and B1 type power grid equipment is not sensitive to temperature differences. At this time, the first temperature data-influence quantity mapping algorithm and the second temperature data-influence quantity mapping algorithm are different algorithms.

[0089] For each external environment data of the target abnormal power grid device, a preset environment data-impact mapping algorithm corresponding to the target external environment data is adopted to determine an impact measure corresponding to the target external environment data; wherein the impact measure represents quantification of negative influence of the corresponding external environment data on the target abnormal power grid device; and then an environment comprehensive influence quantization index used to represent comprehensive influence of the external environment on the target abnormal power grid device is obtained according to the impact measures corresponding to the external environment data.

[0090] In some embodiments, a weighted average, normalization and the like can be introduced to determine the environment comprehensive influence quantization index of the comprehensive influence of the external environment on the target abnormal power grid device. As an example, the environment comprehensive influence quantization index can be calculated by the following formula:

[0091]

[0092]

[0093] wherein H is the environment comprehensive influence quantization index, m is the total number of external environment data of the target abnormal power grid device, W i is the preset influence weight of the external environment factor corresponding to the i-th external environment data on the power grid device, impact i is the impact measure corresponding to the i-th external environment data, and impact_max is the maximum value in the m impact measures corresponding to all m external environment data respectively.

[0094] The formula obtains an environment comprehensive influence quantization index by calculating the comprehensive influence of the external environment on the target abnormal power grid device. W i in the formula is the importance weight of the influence of the external environment factor on the power grid device, which represents the relative contribution of different environment factors to the negative influence on the device, such as temperature rise leading to accelerated device aging. impact_max in the formula is the maximum value of the impact measures corresponding to all external environment data, which is used to normalize the impact measures of each external environment, thereby avoiding deviation caused by different dimensions of the external environment. Finally, the comprehensive influence of all external environments is calculated by weighted summation, and then divided by the maximum impact value to obtain the environment comprehensive influence quantization index. It provides a negative influence level of the device under the current external environment condition, and the lower the value, the smaller the negative influence of the external environment on the device, and the higher the value, the greater the negative influence of the external environment on the device.

[0095] S140, in the installation and debugging node, the second monitoring data of each normal power grid device to be installed and debugged is obtained.

[0096] S150, for each normal power grid equipment, determining the installation and debugging risk evaluation index corresponding to the target normal power grid equipment according to the second monitoring data of the target normal power grid equipment.

[0097] The second monitoring data includes internal electrical data and external environmental data of the normal power grid equipment in the running state. The installation and debugging abnormality evaluation index is used to evaluate the risk brought by the installation process and the debugging process to the safety of the power grid equipment in the installation and debugging node. The higher the installation and debugging abnormality evaluation index is, the greater the probability of abnormality of the power grid equipment is.

[0098] In the installation and debugging node of the material supply chain, the power grid equipment that is not judged as an abnormal power grid equipment in the previous node (for example, the transportation and storage node) is regarded as a normal power grid equipment, and these normal power grid equipments are installed and debugged for subsequent application in the power grid engineering. That is, the abnormal power grid equipment that is judged to be abnormal before the installation and debugging node will not be installed and debugged in the installation and debugging node, that is, the power grid equipment participating in the installation and debugging is the normal power grid equipment by default.

[0099] In step S140, the second monitoring data of each normal power grid equipment in the installation and debugging state is collected in real time through intelligent sensors and Internet of Things devices, and the installation and debugging risk evaluation index corresponding to the second monitoring data is determined; these data include internal electrical data and external environmental data of the equipment.

[0100] It should be noted that in the transportation and storage node, the power grid equipment is in a non-running state, so the external environmental factors affect the power grid equipment. In the installation and debugging node, the power grid equipment is in a running state, so the running of the power grid equipment will affect the external environment around it to a certain extent, so at this time not only the internal power grid data can effectively reflect the running state of the power grid equipment, but also the external environmental data can reflect the running state of the power grid equipment to a certain extent. For example, the power grid equipment is in a high-power running state, at this time the heat dissipation of the power grid equipment will cause the ambient temperature to rise and the strength of the electric field / magnetic field around the power grid equipment to increase.

[0101] In the present disclosure, the second monitoring data can reflect the running state of the normal power grid equipment in real time, thereby embodying the real-time safety of the normal power grid equipment in the installation and debugging process. For example, the current at a certain place in the target normal power grid equipment at a certain moment is far beyond the normal value, which can reflect that the installation and debugging process at the moment is abnormal to a certain extent (for example, there may be violent debugging, or the equipment is not installed normally); for another example, the temperature near the target normal power grid equipment at a certain moment is too low (lower than the normal environmental temperature when the normal power grid equipment is in the running state), which can reflect that the target normal power grid equipment is currently running abnormally (for example, the debugging parameter input error may cause the equipment to be in a low-efficiency running state, thereby causing less heat dissipation and a lower temperature around the equipment; for another example, the equipment may not be installed in place, causing the temperature around the equipment to be low).

[0102] The installation and debugging risk assessment index Q can be obtained according to the following formula:

[0103]

[0104] wherein, is a vector formed by arranging all the internal electrical data and external environmental data in the second monitoring data in a preset order, is a running state reference vector pre-configured for the target normal power grid equipment, has the same dimension as that of , and sim() represents a similarity function (cosine similarity, Pearson similarity, Jaccard similarity, etc. can be adopted, which is not limited in the present disclosure). The greater the value of is, the more similar is, thereby indicating that the running state of the target normal power grid equipment is more normal, the safety influence of the installation process and the debugging process on the target normal power grid equipment is lower, and the probability of abnormality of the target normal power grid equipment is smaller, and the corresponding Q value is smaller; on the contrary, The smaller the value of is, the greater the difference between is, thereby indicating that the running state of the target normal power grid equipment deviates from the normal more seriously, the safety influence of the installation process and the debugging process on the target normal power grid equipment is higher, and the probability of abnormality of the target normal power grid equipment is higher.

[0105] S160, according to the environmental comprehensive influence quantitative index of all abnormal power grid equipment and the installation and debugging maximum risk assessment index of all normal power grid equipment, the safety risk value of the entire power grid engineering material supply chain is evaluated.

[0106] In the present disclosure, in the transportation and storage node, external environmental factors are factors affecting the safety of power grid equipment, by analyzing the influence of external environmental factors on each abnormal power grid equipment, the environmental comprehensive influence quantitative index corresponding to each abnormal power grid equipment is obtained; in the installation and debugging node, installation process and test process are factors affecting the safety of power grid equipment, by the internal electrical data and external environmental data of each normal power grid equipment, the influence of installation process and debugging process on the safety of power grid equipment is analyzed, and the installation and debugging maximum risk evaluation index of each normal power grid equipment is obtained; wherein, the environmental comprehensive influence quantitative index corresponding to each abnormal power grid equipment can effectively reflect the current safety risk of the transportation and storage node in the supply chain, and the installation and debugging maximum risk evaluation index corresponding to each normal power grid equipment can effectively reflect the current safety risk of the installation and debugging node in the supply chain, therefore, based on the environmental comprehensive influence quantitative index corresponding to each abnormal power grid equipment and the installation and debugging maximum risk evaluation index corresponding to each normal power grid equipment, the current safety risk of the entire power grid engineering material supply chain can be effectively reflected.

[0107] In some embodiments, step S160 comprises: evaluating the safety risk value P1 of the transportation and storage node according to the environmental comprehensive influence quantitative index corresponding to each abnormal power grid equipment, and evaluating the safety risk value P2 of the installation and debugging node according to the installation and debugging maximum risk evaluation index corresponding to each normal power grid equipment, and then evaluating the safety risk value P0 of the entire power grid engineering material supply chain according to the safety risk value P1 and the safety risk value P2. That is, P0 = f(P1, P2), f() represents a function about P1 and P2, and P0 is positively correlated with at least one of P1 and P2.

[0108] In some embodiments, the safety risk value of the transportation and storage node is P1:

[0109]

[0110] Wherein, A represents the total number of abnormal power grid equipment in the transportation and storage node, H a represents the environmental comprehensive influence quantitative index of the a-th abnormal power grid equipment, γ a represents the preset importance degree weight coefficient configured for the a-th abnormal power grid equipment.

[0111] The safety risk value of the installation and debugging node is P2:

[0112]

[0113] Wherein, B represents the total number of normal power grid equipment in the installation and debugging node, Q b represents the installation and debugging maximum risk evaluation index of the b-th normal power grid equipment, μ brepresents a preset importance weight coefficient configured by the bth normal power grid device.

[0114] In the present disclosure, P0 is positively correlated with at least one of P1 and P2; in some embodiments, P0 can be expressed as follows:

[0115]

[0116] wherein f() is an alternative function, a, b, g, d are control parameters and only one of the four takes the value 1 and the other three take the value 0; specifically, a is the influence weight of controlling the maximum value calculation method, when P1 or P2 has a particularly prominent effect on P0, a = 1, b = g = d = 0; b is the influence weight of controlling direct summation, when the risk impact is a cumulative effect, b = 1, a = g = d = 0; g is the influence weight of controlling the average value calculation, when the risk assessment needs to be smoothed, g = 1, a = b = d = 0; d is the influence weight of controlling the reweighted summation, when the weight needs to be optimized based on historical data, d = 1, a = b = g = 0. max() is the maximum value function, u1 and u2 are both weighting coefficients, and u1P1 + u2P2 represents the weighted summation of P1 and P2.

[0117] That is, the value of P0 can be the larger of P1 and P2; or the value of P0 is the sum of P1 and P2; or the value of P0 is the average of P1 and P2; or the value of P0 is the weighted summation of P1 and P2. The present disclosure does not limit the algorithm for calculating P0 based on P1 and P2.

[0118] In actual application, based on the above method, the safety risk value of the power grid engineering material supply chain can be dynamically evaluated in real time. Specifically, the above steps S110-S150 are executed once every preset period (for example, 20 seconds, 50 seconds, 1 minute, etc.), which can realize dynamic monitoring of the power grid engineering material supply chain and obtain the safety risk value at the corresponding time. The safety risk value evaluation result reflects the current safety situation of the supply chain, can discover potential fault risks in advance, and helps the operation and maintenance personnel to make timely intervention.

[0119] It should be noted that the technical scheme of the present disclosure does not limit the execution order of steps S110-S130 and steps S140-S150, for example, steps S140-S150 can also be executed before steps S110-S130 or synchronously with steps S110-S130.

[0120] The present embodiment also discloses a monitoring and evaluation device for a power grid engineering material supply chain, which is described with reference to Figure 2The monitoring and evaluation device can be used to implement the monitoring and evaluation method provided in any of the preceding embodiments, and the monitoring and evaluation device comprises a first acquisition module 201, a judgment module 202, a first processing module 203, a second acquisition module 204, a second processing module 205, and an evaluation module 206.

[0121] The first acquisition module 201 is configured to acquire, at a transportation and storage node, first monitoring data of each power grid equipment being transported and stored, wherein the first monitoring data comprises internal electrical data and external environmental data of the power grid equipment in a non-operation state.

[0122] The judgment module 202 is configured to determine, for each power grid equipment, whether the target power grid equipment is abnormal according to the internal electrical data of the target power grid equipment, and confirm the target power grid equipment determined to be abnormal as an abnormal power grid equipment.

[0123] The first processing module 203 is configured to analyze, for each abnormal power grid equipment, a comprehensive influence of external environmental factors on the target abnormal power grid equipment according to all the external environmental data of the target abnormal power grid equipment, and obtain an environmental comprehensive influence quantitative index.

[0124] The second acquisition module 204 is configured to acquire, at an installation and debugging node, second monitoring data of each normal power grid equipment being installed and debugged, wherein the second monitoring data comprises internal electrical data and external environmental data of the normal power grid equipment in an operation state.

[0125] The second processing module 205 is configured to determine, for each normal power grid equipment, an installation and debugging risk evaluation index according to the second monitoring data of the target normal power grid equipment.

[0126] The evaluation module 206 is configured to evaluate a safety risk value of the entire power grid engineering material supply chain according to the environmental comprehensive influence quantitative index of all the abnormal power grid equipment and the installation and debugging maximum risk evaluation index of all the normal power grid equipment.

[0127] In some embodiments, the judgment module 202 is specifically configured to: acquire a normal internal electrical parameter reference data set corresponding to the target power grid equipment, wherein the normal internal electrical parameter reference data set records reference values corresponding to each internal electrical parameter of the target power grid equipment in a non-operation state; match all the internal electrical data of the target power grid equipment collected with the reference values corresponding to the target power grid equipment in the normal internal electrical parameter reference data set, and obtain a matching degree based on a regression algorithm; and determine whether the target power grid equipment is abnormal according to the matching degree.

[0128] Further optionally, the matching degree is determined based on the following formula:

[0129]

[0130] wherein ε is the matching degree, n is the total number of internal electrical data of the target power grid device, x i is the i-th internal electrical data of the target power grid device, s i is the reference value of the electrical parameter corresponding to the i-th internal electrical data of the target power grid device, w i is the weight of the electrical parameter corresponding to the i-th internal electrical data of the target power grid device, and p is the penalty degree of control error.

[0131] In some embodiments, the first processing module 203 is specifically configured to: for each external environmental data of the target abnormal power grid device, determine an impact measure corresponding to the target external environmental data by using a preset environmental data-impact measure mapping algorithm corresponding to the target external environmental data; and calculate an environmental comprehensive impact quantitative index of comprehensive impact according to the impact measures corresponding to the external environmental data.

[0132] In some embodiments, the environmental comprehensive impact quantitative index is determined based on the following formula:

[0133]

[0134]

[0135] wherein H is the environmental comprehensive impact quantitative index, m is the total number of external environmental data of the target abnormal power grid device, W i is the preset impact weight of the external environmental factor corresponding to the i-th external environmental data of the target abnormal power grid device on the power grid device, impact i is the impact measure corresponding to the i-th external environmental data, and impact_max is the maximum value in the m impact measures corresponding to all the m external environmental data, respectively.

[0136] In some embodiments, the second processing module 205 is specifically configured to determine the installation and debugging risk assessment index based on the following formula:

[0137]

[0138] wherein Q is the safety debugging risk assessment index, is a vector formed by arranging all the internal electrical data and external environmental data in the second monitoring data in a preset order, is an operation state reference vector pre-configured for the target normal power grid device, has the same dimension as has the same dimension as

[0139] In some embodiments, the evaluation module is specifically configured to: evaluate a safety risk value P1 of the transportation and storage node according to the environmental comprehensive influence quantification index corresponding to each abnormal power grid equipment; evaluate a safety risk value P2 of the installation and debugging node according to the installation and debugging maximum risk evaluation index corresponding to each normal power grid equipment; and evaluate a safety risk value P0 of the entire power grid engineering material supply chain according to the safety risk value P1 and the safety risk value P2, the safety risk value P0 being positively correlated with at least one of the safety risk value P1 and the safety risk value P2.

[0140] In some embodiments, the safety risk value P1 of the transportation and storage node is:

[0141]

[0142] wherein A represents a total number of abnormal power grid equipment in the transportation and storage node, H a represents the environmental comprehensive influence quantification index of the a-th abnormal power grid equipment, γ a represents a preset importance degree weight coefficient configured for the a-th abnormal power grid equipment;

[0143] The safety risk value P2 of the installation and debugging node is:

[0144]

[0145] wherein B represents a total number of normal power grid equipment in the installation and debugging node, Q b represents the installation and debugging maximum risk evaluation index of the b-th normal power grid equipment, μ b represents a preset importance degree weight coefficient configured for the b-th normal power grid equipment;

[0146] The value of P0 is the larger one of P1 and P2, or the sum of P1 and P2, or the average of P1 and P2, or the weighted sum of P1 and P2.

[0147] It should be noted that the apparatus provided in the above embodiments is only used as an example to illustrate the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0148] The embodiment further discloses an electronic device, which refers to Figure 3 The electronic device can include at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0149] The communication bus 302 is configured to realize the connection communication between the components.

[0150] The user interface 303 can include a display screen, a camera, and optionally, a standard wired interface and a wireless interface.

[0151] The network interface 304 can optionally include a standard wired interface and a wireless interface (e.g., a WI-FI interface).

[0152] The processor 301 can include one or more processing cores. The processor 301 is connected to various parts of the server through various interfaces and lines, and performs various functions and processes data of the server by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU is mainly used to process the operating system, user interface, and application program; the GPU is used to render and draw the content to be displayed on the display screen; and the modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0153] The memory 305 can include a random access memory (RAM) and can also include a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 305 can include a program storage area and a data storage area, where the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area can store data involved in the various method embodiments described above, etc. The memory 305 can also optionally be at least one storage device located away from the aforementioned processor 301. The memory 305, as a computer storage medium, can include an operating system, a network communication module, a user interface 303 module, and an application program of a power grid engineering material supply chain monitoring and evaluation method.

[0154] In Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an interface for user input and obtain data input by the user; and the processor 301 can be used to call an application program of a power grid engineering material supply chain monitoring and evaluation method stored in the memory 305, which, when executed by one or more processors 301, causes the electronic device to perform the method of one or more of the above-described embodiments.

[0155] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0156] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0157] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments is merely illustrative, and the division of units can be changed according to actual needs. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between units can be indirect coupling or communication connection through some intermediary, and can be electrical or other forms.

[0158] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0159] In addition, the functional units in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0160] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium 305 and includes a number of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present application. The aforementioned storage medium 305 includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0161] The present application also discloses a computer readable storage medium, which stores instructions. When executed by one or more processors 301, the electronic device executes the method as described in one or more of the above embodiments.

[0162] The above descriptions are merely some example embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practice of the present disclosure. The present application is intended to cover any variations, uses, or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the art that are not described in the present disclosure. The specification and examples are merely considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for monitoring and evaluating the supply chain of materials for power grid engineering, characterized in that, The power grid engineering material supply chain includes: transportation and storage nodes and installation and commissioning nodes; the monitoring and evaluation methods include: At the transportation and storage node, the first monitoring data of each power grid device being transported and stored is acquired. The first monitoring data includes: internal electrical data and external environmental data of the power grid device in a non-operating state. For each power grid device, determine whether the target power grid device has any abnormality based on its internal electrical data, and identify the target power grid device that has an abnormality as an abnormal power grid device. For each abnormal power grid device, based on all external environmental data of the target abnormal power grid device, the comprehensive impact of external environmental factors on the target abnormal power grid device is analyzed to obtain a comprehensive environmental impact quantification index; During the installation and commissioning phase, second monitoring data of each normal power grid device being installed and commissioned is acquired. The second monitoring data includes: internal electrical data and external environmental data of the normal power grid device in operation. For each normal power grid device, the corresponding installation and commissioning risk assessment indicators are determined based on the second monitoring data of the target normal power grid device; Based on the comprehensive environmental impact quantification index of all abnormal power grid equipment and the maximum risk assessment index of installation and commissioning of all normal power grid equipment, the safety risk value of the entire power grid engineering material supply chain is assessed. The step of determining whether the target power grid equipment has any abnormalities based on its internal electrical data includes: Obtain the normal internal electrical parameter benchmark dataset corresponding to the target power grid equipment. The normal internal electrical parameter benchmark dataset records the benchmark values ​​of each internal electrical parameter of the target power grid equipment in the non-operating state. All the internal electrical data of the target power grid equipment collected are compared and matched with the corresponding benchmark values ​​in the normal internal electrical parameter benchmark dataset of the target power grid equipment, and the degree of matching is obtained based on the regression algorithm. Determine whether the target power grid equipment is abnormal based on the degree of matching; The steps for analyzing the comprehensive impact of external environmental factors on the target abnormal power grid equipment, based on all external environmental data, and obtaining a quantitative index of comprehensive environmental impact, include: For each external environmental data point of the target abnormal power grid equipment, a preset environmental data-influence mapping algorithm corresponding to the target external environmental data is used to determine the influence measure corresponding to the target external environmental data. The comprehensive environmental impact quantification index is calculated based on the impact measures corresponding to each external environmental data. The steps for determining the corresponding installation and commissioning risk assessment indicators based on the second monitoring data of the target normal power grid equipment include: The installation and commissioning risk assessment indicators are determined based on the following formula: ； in, For safety commissioning risk assessment indicators, This is a vector composed of all internal electrical data and external environmental data in the second monitoring data arranged in a preset order. This refers to the pre-configured operating status reference vector for the target normal power grid equipment. Dimensions and The same dimensions Represents the similarity function; The steps for assessing the safety risk value of the entire power grid engineering material supply chain, based on the comprehensive environmental impact quantification index of all abnormal power grid equipment and the maximum risk assessment index for the installation and commissioning of all normal power grid equipment, include: The safety risk value P1 of the transportation and storage nodes is assessed based on the comprehensive environmental impact quantification index corresponding to each abnormal power grid device. ； Where A represents the total number of faulty power grid devices in the transport and storage nodes. This represents the comprehensive environmental impact quantification index of the a-th abnormal power grid device. This represents the preset importance weighting coefficient configured for the a-th abnormal power grid device; The safety risk value P2 of the installation and commissioning nodes is assessed based on the maximum risk assessment index corresponding to the installation and commissioning of each normal power grid equipment: ； Where B represents the total number of normal power grid devices in the installation and commissioning nodes, This represents the maximum risk assessment index for the installation and commissioning of the b-th normal power grid equipment. This represents the preset importance weighting coefficient configured for the b-th normal power grid device; The safety risk value P0 of the entire power grid engineering material supply chain is assessed based on the safety risk values ​​P1 and P2. P0 is positively correlated with at least one of P1 and P2.

2. The monitoring and evaluation method according to claim 1, characterized in that, The steps of comparing and matching all the collected internal electrical data of the target power grid equipment with the corresponding benchmark values ​​in the normal internal electrical parameter benchmark dataset of the target power grid equipment, and obtaining the degree of matching based on a regression algorithm, include: The degree of matching is determined based on the following formula: ； Where ε represents the matching degree, n represents the total number of internal electrical data of the target power grid equipment, and x i For the i-th internal electrical data of the target power grid device, s i w is the reference value of the electrical parameter corresponding to the i-th internal electrical data of the target power grid equipment. i denoted as the weight of the electrical parameter corresponding to the i-th internal electrical data of the target power grid equipment, and p is the penalty degree of the control error and takes a positive integer value.

3. The monitoring and evaluation method according to claim 1, characterized in that, The steps for calculating the comprehensive environmental impact quantification index based on the impact measures corresponding to various external environmental data include: The comprehensive environmental impact quantification index is determined based on the following formula: ； ； Where H is the comprehensive environmental impact quantification index, m is the total number of external environmental data for the target abnormal power grid equipment, and W i The preset impact weight of the external environmental factors corresponding to the i-th external environmental data on the power grid equipment. i Let be the impact measure corresponding to the i-th external environment data, and let impact_max be the maximum value among the m impact measures corresponding to all m external environment data.

4. A monitoring and evaluation device for the power grid engineering material supply chain, characterized in that, The monitoring and evaluation device is used to implement any one of the monitoring and evaluation methods according to claims 1 to 3, and the monitoring and evaluation device includes: The first acquisition module (201) is configured to acquire first monitoring data of each power grid device being transported and stored at the transport and storage node. The first monitoring data includes: internal electrical data and external environmental data of the power grid device in a non-operating state. The judgment module (202) is configured to, for each power grid device, determine whether the target power grid device is abnormal based on the internal electrical data of the target power grid device, and confirm the target power grid device that is determined to be abnormal as an abnormal power grid device; The first processing module (203) is configured to, for each abnormal power grid device, analyze the comprehensive impact of external environmental factors on the target abnormal power grid device based on all external environmental data of the target abnormal power grid device, and obtain a comprehensive environmental impact quantification index; The second acquisition module (204) is configured to acquire the second monitoring data of each normal power grid device installed and commissioned in the installation and commissioning node. The second monitoring data includes: internal electrical data and external environmental data of the normal power grid device in operation. The second processing module (205) is configured to determine the corresponding installation and commissioning risk assessment indicators for each normal power grid device based on the second monitoring data of the target normal power grid device. The assessment module (206) is configured to assess the safety risk value of the entire power grid engineering material supply chain based on the comprehensive environmental impact quantification index of all abnormal power grid equipment and the maximum risk assessment index of installation and commissioning of all normal power grid equipment.

5. An electronic device, characterized in that, The device includes a processor (301), a communication bus (302), a user interface (303), a network interface (304), and a memory (305). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are both used to communicate with other devices. The communication bus (302) is used to realize the connection and communication between the components within the electronic device. The processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device performs the monitoring and evaluation method as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the monitoring and evaluation method as described in any one of claims 1-3.

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