Distribution network material sampling inspection strategy generation method based on differential sampling inspection strategy model

Abstract

The invention belongs to the technical field of risk prediction of a specific computer model, and relates to a distribution network material sampling inspection strategy generation method based on a differentiated sampling inspection strategy model, which comprises the following steps: acquiring quality information of five stages of bid procurement, production and manufacturing, product delivery, construction and installation, maintenance and operation of distribution network equipment in a full life cycle; performing basic configuration on the quality information of the five stages to obtain full life cycle quality information; analyzing the full-life-cycle quality information according to a preset evaluation standard and quality label data to obtain a full-life-cycle quality evaluation coefficient; the full life cycle quality evaluation coefficient is input into a differential sampling inspection strategy model, and the differential sampling inspection strategy model outputs a corresponding dynamic sampling inspection strategy. The adjustment coefficient in the full life cycle quality evaluation coefficient can be dynamically adjusted according to the actual situation so as to adapt to different market environments, customer demands and supply chain changes. Due to the flexibility, the evaluation result is closer to reality and has higher reference value.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk prediction of specific computer models and relates to a method for generating a distribution network material sampling strategy based on a differentiated sampling strategy model. Background Art

[0002] With the continuous deepening of the construction of the green modern supply chain, asset life cycle management, and equipment full process management of the State Grid, the random inspection of power materials has become an important quality supervision and control means for the arrival and delivery of power grid materials during the full life cycle. At present, there are few methods for formulating distribution material sampling strategies, lacking scientific analysis and mathematical statistics of data from the perspective of the full life cycle. It is mainly based on three 100% full coverage, and uniformly formulates sampling quotas every year, which are implemented by random sampling. With the progress of material sampling, a large amount of historical data has been accumulated. Simple quotas and random sampling can no longer meet the needs of current sampling work. Existing technologies cannot be dynamically adjusted according to actual conditions, and the sampling results lack accuracy and flexibility. It is urgent to mine the data value through the full life cycle of distribution network material quality, formulate differentiated sampling strategies scientifically and efficiently in the random inspection of State Grid distribution materials, and effectively improve the quality level of distribution network materials. Summary of the invention

[0003] The purpose of the present invention is to provide a distribution network material sampling strategy generation method based on a differentiated sampling strategy model to solve the technical problems that the sampling method in the prior art cannot be dynamically adjusted according to actual conditions and the sampling results lack accuracy and flexibility.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A distribution network material sampling strategy generation method based on a differentiated sampling strategy model includes: Obtain quality information on the five stages of the distribution network equipment life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation; The quality information of the five stages is configured in a basic manner to obtain the quality information of the entire life cycle; Analyze the quality information of the entire life cycle according to the preset evaluation standards and quality label data to obtain the quality evaluation coefficient of the entire life cycle; The full life cycle quality evaluation coefficient is input into the differentiated sampling strategy model, and the differentiated sampling strategy model outputs the corresponding dynamic sampling strategy.

[0005] Preferably, the full life cycle quality information includes material category dimension information, supplier dimension information and random inspection items.

[0006] Preferably, the quality label data includes: material category dimension quality label data and supplier dimension quality label data; The material category dimension quality label data includes: procurement scale, raw material price, new products, cloud supervision, inspection cycle, sampling quality level and operation quality level; The supplier dimension quality label data includes: winning bid scale, winning bid price, network registration, cloud supervision, delivery cycle, random inspection quality level and operation quality level.

[0007] Preferably, the whole life cycle quality information is analyzed according to a preset evaluation standard and quality label data to obtain a whole life cycle quality evaluation coefficient, specifically including: The whole life cycle quality information and the corresponding quality label data are configured to obtain the quality label data information of the material category dimension and the quality label data information of the supplier dimension; The material category dimension quality label information and the supplier dimension quality label data information are combined with the preset evaluation criteria to obtain the material category dimension adjusted coefficient and the supplier dimension adjusted coefficient; The full life cycle quality evaluation coefficient is obtained based on the adjusted coefficient of the material category dimension and the adjusted coefficient of the supplier dimension, where the full life cycle quality evaluation coefficient = the adjusted coefficient of the material category dimension × the adjusted coefficient of the supplier dimension.

[0008] Preferably, the differentiated sampling strategy model includes three differentiated sampling strategy preferences, specifically: basic preference, quality efficiency preference and quality assurance preference; The sampling strategies corresponding to the basic preferences are: sampling batches, material types, suppliers and unified sampling quotas; The sampling strategies corresponding to the quality and efficiency preferences are: sampling batches, material types, suppliers and differentiated sampling quotas; The sampling strategies corresponding to the quality assurance preferences include: 1) Inspection batches, material types, suppliers, all models and differentiated inspection quotas; 2) Inspection batches, material types, suppliers, all orders and differentiated inspection quotas; 3) Sampling batches, material types, suppliers, all models, all orders and differentiated sampling quotas.

[0009] Preferably, the unified sampling quota is fixed mandatory inspection items, sampling ratios, quantities and frequencies of ABC categories; The differentiated sampling quota determines the corresponding sampling items, sampling ratio, quantity and frequency based on the adjusted coefficient of the material category dimension, the adjusted coefficient of the supplier dimension and the full life cycle quality evaluation coefficient.

[0010] Preferably, the adjusted coefficient of the material category dimension and the adjusted coefficient of the supplier dimension are both the basic coefficient plus the adjustment coefficient; the basic coefficient is 1, and the maximum adjustment coefficient is 1.

[0011] In the second aspect, the present application discloses a distribution network material sampling strategy generation system based on a differentiated sampling strategy model, including: The information acquisition module is used to obtain quality information of the five stages of the distribution network equipment life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation; An information configuration module is used to perform basic configuration on the quality information of the five stages to obtain the quality information of the entire life cycle; The information analysis module is used to analyze the quality information of the entire life cycle according to the preset evaluation standards and quality label data to obtain the quality evaluation coefficient of the entire life cycle; The strategy generation module is used to input the differentiated sampling strategy model of the full life cycle quality evaluation coefficient, and the differentiated sampling strategy model outputs the corresponding dynamic sampling strategy; and according to the sampling strategy, the differentiated sampling strategy model is dynamically optimized.

[0012] In a third aspect, the present application discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-mentioned methods for generating a distribution network material sampling strategy based on a differentiated sampling strategy model when executing the computer program.

[0013] In a fourth aspect, the present application discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method for generating a distribution network material sampling strategy based on a differentiated sampling strategy model as described in any of the above items.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1) This application obtains the life cycle quality evaluation coefficient by analyzing the life cycle quality information according to the preset evaluation standards and quality label data. The adjustment coefficient in the life cycle quality evaluation coefficient can be dynamically adjusted according to the actual situation to adapt to different market environments, customer needs and supply chain changes. This flexibility makes the evaluation results closer to reality and more valuable for reference.

[0015] 2) This application is based on a differentiated sampling strategy model and formulates five differentiated sampling strategies of "less inspection for high quality and more inspection for low quality" in accordance with the sampling strategy preferences (basic preference, quality efficiency preference, and quality assurance preference), including material type inspection items, supplier sampling ratio, quantity, and frequency, to achieve accurate formulation and dynamic adjustment of sampling plans and tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 is a flow chart of the method of the present invention; Figure 2 A quality label data category diagram according to an embodiment of the present invention; Figure 3 It is a quality evaluation coefficient diagram of various distribution network material category dimensions and supplier dimensions in an embodiment of the present invention; Figure 4 This is a label diagram of the three-level procurement scale data according to the quartile method of an embodiment of the present invention; Figure 5 This is a label diagram of “Purchase Scale” of an embodiment of the present invention; Figure 6 A diagram of the quality evaluation coefficient of a distribution transformer according to an embodiment of the present invention; Figure 7 This is a diagram of evaluation coefficients of a distribution transformer according to an embodiment of the present invention; Figure 8 is a quality evaluation coefficient diagram of an embodiment of the present invention; Fig. 9 This is a model diagram of a differentiated sampling strategy according to an embodiment of the present invention; Fig.10 This is a distribution transformer sampling strategy diagram according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0021] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0022] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0023] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] The present invention is further described in detail below in conjunction with the accompanying drawings: See also Figure 1 The present application discloses a method for generating a distribution network material sampling inspection strategy based on a differentiated sampling inspection strategy model, comprising: S1: Obtain quality information on the five stages of the distribution network equipment life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation; S2: Perform basic configuration on the quality information of the five stages to obtain the quality information of the whole life cycle; S3: Analyze the quality information of the entire life cycle according to the preset evaluation standards and quality label data to obtain the quality evaluation coefficient of the entire life cycle; S4: The full life cycle quality evaluation coefficient is input into the differentiated sampling strategy model, and the differentiated sampling strategy model outputs the corresponding dynamic sampling strategy. This application obtains the full life cycle quality evaluation coefficient by analyzing the full life cycle quality information according to the preset evaluation standards and quality label data. The adjustment coefficient in the full life cycle quality evaluation coefficient can be dynamically adjusted according to actual conditions to adapt to different market environments, customer needs and supply chain changes. This flexibility makes the evaluation results closer to reality and more valuable for reference. Based on the differentiated sampling strategy model, this application formulates 5 types of differentiated "less inspections for high quality, more inspections for poor quality" sampling strategies according to the sampling strategy preferences (basic preference, quality efficiency preference, quality assurance preference), including material type inspection items, supplier sampling ratio, quantity, and frequency, to achieve accurate formulation and dynamic adjustment of sampling plans and sampling tasks. In some embodiments, the full life cycle quality information includes material category dimension information, supplier dimension information and random inspection items.

[0025] In some embodiments, obtaining quality information of the five stages of the distribution network equipment's entire life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation, is specifically as follows: sorting out and analyzing the quality data needs of the entire life cycle, collecting data through the data middle platform, and conducting detailed data configuration.

[0026] Step S1.1 Sort out and analyze the material procurement, supervision, sampling information related to the five stages of the distribution network equipment's life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation, as well as equipment defects, failures, hidden danger records and other material quality life cycle data requirements.

[0027] Step S1.2 collects quality information throughout the life cycle through the Beijing data center, including: ① structured information such as the qualified status of sampling items, sampling problem levels, sampling reports, etc. (testing business system of the Institute of Electrical Engineering), ② sampling plans, sampling tasks, basic information of Beijing suppliers, supplier misconduct, etc. (e-commerce platform ESC, Beijing supply chain operation platform EOP), ③ information such as supply cycle, main raw materials matching material types, etc. (ERP materials), ④ equipment and material operation defect data, supplier operation failures, and defect evaluation information (equipment lean PMS).

[0028] Step S1.3 will collect the quality information of the whole life cycle for basic configuration, ① random inspection material category configuration, including random inspection material classification configuration, random inspection material scope configuration, random inspection material category and equipment category association configuration, material and main raw material matching configuration, etc., ② random inspection test item configuration, including AbC test items, basic test cycle, test item quota, etc., ③ supplier information configuration, including supplier basic attributes, material domain supplier and equipment domain supplier, manufacturer association configuration.

[0029] In some embodiments, see Figure 2 , the quality label data includes: material category dimension quality label data and supplier dimension quality label data; The material category dimension quality label data includes: procurement scale, raw material price, new products, cloud supervision, inspection cycle, sampling quality level and operation quality level; The supplier dimension quality label data includes: winning bid scale, winning bid price, network registration, cloud supervision, delivery cycle, random inspection quality level and operation quality level.

[0030] In some embodiments, the whole life cycle quality information is analyzed according to a preset evaluation standard and quality label data to obtain a whole life cycle quality evaluation coefficient, specifically including: The whole life cycle quality information and the corresponding quality label data are configured to obtain the quality label data information of the material category dimension and the quality label data information of the supplier dimension; The material category dimension quality label information and the supplier dimension quality label data information are combined with the preset evaluation criteria to obtain the material category dimension adjusted coefficient and the supplier dimension adjusted coefficient; The full life cycle quality evaluation coefficient is obtained based on the adjusted coefficient of the material category dimension and the adjusted coefficient of the supplier dimension, where the full life cycle quality evaluation coefficient = the adjusted coefficient of the material category dimension × the adjusted coefficient of the supplier dimension.

[0031] The basic quality evaluation coefficient of each type of distribution network material category dimension and supplier dimension is 1, and the maximum quality evaluation adjustment coefficient of each type of distribution network material category dimension and supplier dimension is 1. The maximum value of the basic coefficient plus the adjustment coefficient is 2.

[0032] In some embodiments, the differentiated sampling strategy model includes three differentiated sampling strategy preferences, specifically: basic preference, quality efficiency preference, and quality assurance preference; The sampling strategies corresponding to the basic preferences are: sampling batches, material types, suppliers and unified sampling quotas; The sampling strategies corresponding to the quality and efficiency preferences are: sampling batches, material types, suppliers and differentiated sampling quotas; The sampling strategies corresponding to the quality assurance preferences include: 1) Inspection batches, material types, suppliers, all models and differentiated inspection quotas; 2) Inspection batches, material types, suppliers, all orders and differentiated inspection quotas; 3) Sampling batches, material types, suppliers, all models, all orders and differentiated sampling quotas.

[0033] In some embodiments, the unified sampling quota is fixed mandatory inspection items, sampling ratios, quantities and frequencies of ABC categories; The differentiated sampling quota determines the corresponding sampling items, sampling ratio, quantity and frequency based on the adjusted coefficient of the material category dimension, the adjusted coefficient of the supplier dimension and the full life cycle quality evaluation coefficient.

[0034] In some embodiments, a method for generating a distribution network material sampling strategy based on a differentiated sampling strategy model is characterized by comprising: Obtain quality information on the five stages of the distribution network equipment life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation; The quality information of the five stages is configured in a basic manner to obtain the quality information of the entire life cycle; Analyze the quality information of the entire life cycle according to the preset evaluation standards and quality label data to obtain the quality evaluation coefficient of the entire life cycle; The full life cycle quality evaluation coefficient is input into the differentiated sampling strategy model. The differentiated sampling strategy model determines the differentiated sampling strategy preference according to the full life cycle quality evaluation coefficient, and outputs the dynamic sampling strategy corresponding to the full life cycle quality evaluation coefficient in the differentiated sampling strategy preference; The differentiated sampling strategy model includes three differentiated sampling strategy preferences, specifically: basic preference, quality efficiency preference and quality assurance preference; The sampling strategies corresponding to the basic preferences are: sampling batches, material types, suppliers and unified sampling quotas; The sampling strategies corresponding to the quality and efficiency preferences are: sampling batches, material types, suppliers and differentiated sampling quotas; The sampling strategies corresponding to the quality assurance preferences include: 1) Inspection batches, material types, suppliers, all models and differentiated inspection quotas; 2) Inspection batches, material types, suppliers, all orders and differentiated inspection quotas; 3) Sampling batches, material types, suppliers, all models, all orders and differentiated sampling quotas.

[0035] [Example] See also Figure 3 , taking the "Purchase Scale" label analysis of distribution transformers as an example, as of the third quarter of 2024, the scale of distribution network procurement materials procurement this year is analyzed, see Figure 4 , according to the quartile method to point out the three-level procurement scale data labels; see Figure 5 , the "procurement scale" label of distribution transformers is "core materials", and the quality evaluation adjustment coefficient is "0.1" See also Figure 6, Figure 7 , mining quality label data of material category dimensions (purchase scale, raw material price, new products, cloud supervision, inspection cycle, sampling quality level, operation quality level); mining quality label data of supplier dimensions (bid scale, bid price, network registration, cloud supervision, supply cycle, sampling quality level, operation quality level); See also Figure 8 , calculate the quality evaluation results of the whole life cycle of each type of material, and the quality evaluation coefficient of the whole life cycle = the adjusted coefficient of the material category dimension × the adjusted coefficient of the supplier dimension.

[0036] Based on the three sampling strategies of 100% full coverage and sampling quota, differentiated sampling strategies for each type of materials are adjusted and formulated according to the quality evaluation results.

[0037] A preliminary sampling strategy based on three 100% coverage and sampling quotas has been formulated. The three 100% coverage includes (each arrival batch, material type, and supplier), and the sampling quota is issued by the State Grid headquarters every year, stipulating the ABC category mandatory inspection items, sampling ratio, quantity, and frequency.

[0038] See also Fig. 9 , formulate differentiated sampling strategies of "less inspection for high-quality materials and more inspection for low-quality materials" for each type of materials according to sampling strategy preferences (basic preference, quality efficiency preference, quality assurance preference). It includes the following 5 categories: Three 100% coverage (batch, material type, supplier) + unified sampling quota Three 100% coverage (batch, material type, supplier) + differentiated sampling quota Four 100% coverage (batch, material type, supplier, all models) + differentiated sampling quota Four 100% coverage (batch, material type, supplier, all orders) + differentiated sampling quota Five 100% coverage (batch, material type, supplier, all models, all orders) + differentiated sampling quota According to the above distribution transformer quality evaluation adjustment coefficient of 1.82, quality and efficiency preference is adopted, and the sampling strategy of three 100% coverage (batch, material type, supplier) + differentiated sampling quota is implemented. Fig.10 , the sampling tasks are clearly defined as ① the sampling items are "sampling quota benchmark items", ② the sampling batches are "one sampling per batch per order", ③ the sampling quantity is "basic quantity + 1 sampling", and ④ the supplier sampling frequency is "sampling quota benchmark sampling frequency + 1" In some embodiments, a distribution network material sampling strategy generation method based on a differentiated sampling strategy model is provided. The method obtains the quality information of the five stages of the distribution network equipment's life cycle, namely, bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation, and establishes a quality evaluation standard for the material life cycle from the material category and supplier dimensions. On the basis of three 100% full coverage and sampling quota-based sampling strategies, the quality label data of the material category dimension (procurement scale, raw material price, new product, cloud monitoring, inspection cycle, sampling quality level, and operation quality level) and the supplier dimension (bid scale, bid price, registration and access to the network, cloud monitoring, supply cycle, sampling quality level, and operation quality level) are mined to formulate differentiated sampling strategies for each material category. The beneficial effect of implementing the present invention is that according to the sampling strategy preference (basic preference, quality efficiency preference, quality assurance preference), 5 types of differentiated "less inspection for high quality, more inspection for low quality" sampling strategies are formulated, including material category inspection items, supplier sampling ratio, quantity, and frequency, so as to achieve accurate formulation and dynamic adjustment of sampling plans and sampling tasks.

[0039] In some embodiments, a method for generating a distribution network material sampling strategy based on a differentiated sampling strategy model includes: Step 1: Obtain the data related to the five stages of the distribution network equipment's life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation. Obtain the data on the material quality of the distribution network equipment throughout its life cycle, including procurement, supervision, and sampling information, equipment defects, failures, hidden dangers, and other material quality life cycle data.

[0040] Step 2: Establish evaluation standards, mine quality label data, and conduct full life cycle quality evaluation of each type of distribution network materials.

[0041] Step 2.1: Establish quality evaluation standards for the entire life cycle of materials from the material category and supplier dimensions. The basic quality evaluation coefficient of the material category and supplier dimensions of various distribution network materials is 1; the maximum quality evaluation adjustment coefficient of the material category and supplier dimensions of various distribution network materials is 1. The maximum value of the basic coefficient plus the adjustment coefficient is 2.

[0042] Step 2.2: Mining quality label data of material category dimensions (procurement scale, raw material price, new products, cloud supervision, inspection cycle, sampling quality level, and operation quality level).

[0043] Step 2.3: Mine the quality label data of supplier dimensions (bid size, bid price, network registration, cloud supervision, delivery cycle, sampling quality level, and operation quality level).

[0044] Step 2.4: Calculate the quality evaluation results of each type of material throughout its life cycle.

[0045] The final quality evaluation coefficient is calculated by the adjusted coefficient of the material category dimension * the adjusted coefficient of the supplier dimension.

[0046] Step 3: Based on the three sampling strategies of 100% full coverage and sampling quota, formulate differentiated sampling strategies for each type of materials according to the quality evaluation results.

[0047] Step 3.1: Preliminary formulation of three 100% full coverage and sampling quota-based sampling strategies. The three 100% coverage includes (each arrival batch, material type, and supplier), and the sampling quota is issued by the State Grid headquarters every year, stipulating the ABC category mandatory inspection items, sampling ratio, quantity, and frequency.

[0048] Step 3.2: According to the sampling strategy preferences (basic preference, quality preference, quality efficiency preference), formulate differentiated sampling strategies of "less inspection for high-quality materials and more inspection for low-quality materials" for each type of materials. Including the following 5 categories: Three 100% coverage (batch, material type, supplier) + unified sampling quota Three 100% coverage (batch, material type, supplier) + differentiated sampling quota Four 100% coverage (batch, material type, supplier, all models) + differentiated sampling quota Four 100% coverage (batch, material type, supplier, all orders) + differentiated sampling quota Five 100% coverage (batch, material type, supplier, all models, all orders) + differentiated sampling quota Step 4: Accurately formulate and dynamically adjust sampling plans and tasks based on differentiated sampling strategies.

[0049] The present application also discloses a distribution network material sampling inspection strategy generation system based on a differentiated sampling inspection strategy model, including: The information acquisition module is used to obtain quality information of the five stages of the distribution network equipment life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation; An information configuration module is used to perform basic configuration on the quality information of the five stages to obtain the quality information of the entire life cycle; The information analysis module is used to analyze the quality information of the entire life cycle according to the preset evaluation standards and quality label data to obtain the quality evaluation coefficient of the entire life cycle; The strategy generation module is used to input the differentiated sampling strategy model of the full life cycle quality evaluation coefficient, and the differentiated sampling strategy model outputs the corresponding dynamic sampling strategy; and according to the sampling strategy, the differentiated sampling strategy model is dynamically optimized.

[0050] In some embodiments, a distribution network material sampling strategy generation system based on a differentiated sampling strategy model includes: The information acquisition module is used to obtain quality information of the five stages of the distribution network equipment life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation; The configuration is: collect quality information throughout the life cycle through the Beijing data center, ① structured information such as the qualified status of sampling items, sampling problem levels and sampling reports (testing business system of the Institute of Electrical Engineering), ② sampling plans, sampling tasks, basic information of Beijing suppliers, supplier misconduct and other information (e-commerce platform ESC, Beijing supply chain operation platform EOP), ③ supply cycle, main raw materials matching material types and other information (ERP materials), ④ equipment and material operation defect data, supplier operation failures, defect evaluation information (equipment lean PMS).

[0051] An information configuration module is used to perform basic configuration on the quality information of the five stages to obtain the quality information of the entire life cycle; The configuration is as follows: basic configuration will be performed on the quality information collected throughout the life cycle, ① configuration of sampling material categories, including classification configuration of sampling material, configuration of sampling material scope, association configuration of sampling material categories and equipment categories, matching configuration of materials and main raw materials, etc.; ② configuration of sampling inspection items, including AbC inspection items, basic inspection cycle, quota of inspection items, etc.; ③ configuration of supplier information, including basic attributes of suppliers, association configuration of material domain suppliers and equipment domain suppliers, and manufacturer.

[0052] The information analysis module is used to analyze the quality information of the entire life cycle according to the preset evaluation standards and quality label data to obtain the quality evaluation coefficient of the entire life cycle; Configuration: For the configured consistent quality information of the entire life cycle, start data statistical analysis and label it. ① Sampling material category dimension analysis, including procurement scale analysis, procurement price main raw material correlation analysis, inspection cycle analysis, sampling quality level analysis, and operation quality level analysis; ② Supplier dimension analysis, including bid scale analysis, bid price analysis, supply cycle analysis, sampling quality level analysis, and operation quality level analysis.

[0053] The strategy generation module is used to input the differentiated sampling strategy model of the full life cycle quality evaluation coefficient, and the differentiated sampling strategy model outputs the corresponding dynamic sampling strategy; and according to the sampling strategy, the differentiated sampling strategy model is dynamically optimized.

[0054] The configuration is: based on the label data of material categories and supplier dimensions, establish a sampling strategy library model to generate differentiated sampling strategies; dynamically optimize the sampling strategy library model based on the execution of the sampling strategy; and actively recommend the optimal sampling strategy when formulating sampling plans and tasks.

[0055] The present application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for generating a distribution network material sampling strategy based on a differentiated sampling strategy model as described in any one of the above items are implemented.

[0056] The present application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for generating a distribution network material sampling strategy based on a differentiated sampling strategy model as described in any of the above items are implemented.

[0057] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0059] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distribution network material sampling strategy generation method based on a differentiated sampling strategy model, characterized in that: include: Obtain quality information on the five stages of the distribution network equipment life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation; The quality information of the five stages is configured in a basic manner to obtain the quality information of the entire life cycle; Analyze the quality information of the entire life cycle according to the preset evaluation standards and quality label data to obtain the quality evaluation coefficient of the entire life cycle; The full life cycle quality evaluation coefficient is input into the differentiated sampling strategy model, and the differentiated sampling strategy model outputs the corresponding dynamic sampling strategy.

2. According to claim 1, a method for generating a distribution network material sampling inspection strategy based on a differentiated sampling inspection strategy model is characterized in that: The full life cycle quality information includes material category dimension information, supplier dimension information and random inspection and testing items.

3. According to claim 1, a method for generating a distribution network material sampling inspection strategy based on a differentiated sampling inspection strategy model is characterized in that: The quality label data includes: material category dimension quality label data and supplier dimension quality label data; The material category dimension quality label data includes: procurement scale, raw material price, new products, cloud supervision, inspection cycle, sampling quality level and operation quality level; The supplier dimension quality label data includes: winning bid scale, winning bid price, network registration, cloud supervision, delivery cycle, random inspection quality level and operation quality level.

4. According to claim 3, a method for generating a distribution network material sampling inspection strategy based on a differentiated sampling inspection strategy model is characterized in that: The whole life cycle quality information is analyzed according to the preset evaluation standard and quality label data to obtain the whole life cycle quality evaluation coefficient, which specifically includes: The whole life cycle quality information and the corresponding quality label data are configured to obtain the quality label data information of the material category dimension and the quality label data information of the supplier dimension; The material category dimension quality label information and the supplier dimension quality label data information are combined with the preset evaluation criteria to obtain the material category dimension adjusted coefficient and the supplier dimension adjusted coefficient; The full life cycle quality evaluation coefficient is obtained based on the adjusted coefficient of the material category dimension and the adjusted coefficient of the supplier dimension, where the full life cycle quality evaluation coefficient = the adjusted coefficient of the material category dimension × the adjusted coefficient of the supplier dimension.

5. According to claim 1, a method for generating a distribution network material sampling inspection strategy based on a differentiated sampling inspection strategy model is characterized in that: The differentiated sampling strategy model includes three differentiated sampling strategy preferences, specifically: basic preference, quality efficiency preference and quality assurance preference; The sampling strategies corresponding to the basic preferences are: sampling batches, material types, suppliers and unified sampling quotas; The sampling strategies corresponding to the quality and efficiency preferences are: sampling batches, material types, suppliers and differentiated sampling quotas; The sampling strategies corresponding to the quality assurance preferences include: 1) Inspection batches, material types, suppliers, all models and differentiated inspection quotas; 2) Inspection batches, material types, suppliers, all orders and differentiated inspection quotas; 3) Sampling batches, material types, suppliers, all models, all orders and differentiated sampling quotas.

6. The method for generating a distribution network material sampling inspection strategy based on a differentiated sampling inspection strategy model according to claim 5 is characterized in that: The unified sampling quota is a fixed set of mandatory inspection items, sampling ratios, quantities and frequencies for categories ABC; The differentiated sampling quota determines the corresponding sampling items, sampling ratio, quantity and frequency based on the adjusted coefficient of the material category dimension, the adjusted coefficient of the supplier dimension and the full life cycle quality evaluation coefficient.

7. According to claim 4, a method for generating a distribution network material sampling inspection strategy based on a differentiated sampling inspection strategy model is characterized in that: The adjusted coefficient of the material category dimension and the adjusted coefficient of the supplier dimension are both the basic coefficient plus the adjustment coefficient; the basic coefficient is 1, and the maximum adjustment coefficient is 1.

8. A distribution network material sampling strategy generation system based on a differentiated sampling strategy model, characterized in that: include: The information acquisition module is used to obtain quality information of the five stages of the distribution network equipment life cycle, including bidding and procurement, manufacturing, product delivery, construction and installation, and maintenance and operation; An information configuration module is used to perform basic configuration on the quality information of the five stages to obtain the quality information of the entire life cycle; The information analysis module is used to analyze the quality information of the entire life cycle according to the preset evaluation standards and quality label data to obtain the quality evaluation coefficient of the entire life cycle; The strategy generation module is used to input the differentiated sampling strategy model of the full life cycle quality evaluation coefficient, and the differentiated sampling strategy model outputs the corresponding dynamic sampling strategy; and according to the sampling strategy, the differentiated sampling strategy model is dynamically optimized.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for generating a distribution network material sampling strategy based on a differentiated sampling strategy model as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for generating a distribution network material sampling strategy based on a differentiated sampling strategy model as described in any one of claims 1 to 7.

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