Quality evaluation method and system, electronic equipment and storage medium

Abstract

The invention provides a quality evaluation method, which is applied to the technical field of big data. The method is applied to a quality assessment system, the quality assessment system comprises at least one quality risk assessment model, each quality risk assessment model corresponds to a cargo type, and the method comprises the steps of collecting cargo data for a target port in real time, the cargo data comprising cargo data of at least one cargo type; for a target cargo type in the at least one cargo type, determining a target quality risk assessment model corresponding to the target cargo type from the at least one quality risk assessment model; and performing quality risk assessment on the cargo data of the target cargo type by using the target quality risk assessment model, and outputting a cargo quality assessment result for the target cargo type. The invention further provides a system, electronic equipment and a storage medium.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, in particular to the field of quality supervision, and more specifically to a quality assessment method, system, electronic device, storage medium and program product. Background Art

[0002] In the import of bulk goods, it is necessary to assess the quality and safety risks of bulk goods. The relevant technology mainly uses product sampling and testing and manual inspection methods such as touch, visual inspection, and nose sniffing to conduct quality assessment. However, the sampling volume of bulk goods is large, and manual inspection and supervision are difficult to cover the entire process. In addition, the efficiency of manual inspection is low, which easily leads to low port clearance efficiency. Summary of the invention

[0003] In view of this, the present disclosure provides a quality assessment method, system, electronic device, storage medium and program product.

[0004] One aspect of the present disclosure provides a quality assessment method, which is applied to a quality assessment system, wherein the quality assessment system includes at least one quality risk assessment model, each quality risk assessment model corresponds to a type of goods, and the method includes:

[0005] Collect cargo data for a target port in real time, wherein the cargo data includes cargo data of at least one cargo type;

[0006] For a target cargo type among the at least one cargo type, determining a target quality risk assessment model corresponding to the target cargo type from at least one quality risk assessment model; and

[0007] The target quality risk assessment model is used to conduct quality risk assessment on the cargo data of the target cargo type, and the cargo quality assessment results for the target cargo type are output.

[0008] According to an embodiment of the present disclosure, the target quality risk assessment model includes at least one assessment indicator and an indicator assessment rule corresponding to each assessment indicator;

[0009] The target quality risk assessment model is used to conduct quality risk assessment on the cargo data of the target cargo type, and the cargo quality assessment results for the target cargo type are output including:

[0010] For an i-th evaluation indicator among the at least one evaluation indicator, extracting i-th indicator data corresponding to the i-th evaluation indicator from cargo data of the target cargo type;

[0011] Using the i-th indicator evaluation rule corresponding to the i-th evaluation indicator, evaluate the i-th indicator data, and output the indicator evaluation result corresponding to the i-th evaluation indicator;

[0012] The goods quality assessment results are determined based on the respective assessment results of each assessment indicator.

[0013] According to an embodiment of the present disclosure, the goods quality evaluation result is determined according to the evaluation result of each evaluation indicator, including:

[0014] According to the preset quality assessment rules, the evaluation results of each evaluation indicator are analyzed to obtain the cargo quality assessment results.

[0015] According to an embodiment of the present disclosure, the above method further includes:

[0016] Determine a target terminal display device from at least one terminal display device according to a preset evaluation result push path;

[0017] Push the cargo quality assessment results to the target terminal display device.

[0018] According to an embodiment of the present disclosure, before pushing the goods quality assessment result to the target terminal display device, it also includes:

[0019] Adjusting the format of the goods quality assessment result according to the display mode of the target terminal display device to obtain an adjusted goods quality assessment result;

[0020] The goods quality assessment results are pushed to the target terminal display device including:

[0021] Send the adjusted goods quality assessment results to the target terminal display device.

[0022] According to an embodiment of the present disclosure, the display method may include at least one of the following: pop-up display, voice broadcast prompt, text display, and scrolling display.

[0023] According to an embodiment of the present disclosure, the above method further includes:

[0024] In response to an optimization operation on the target quality risk assessment model, an optimized target quality risk assessment model is generated, wherein the optimization operation includes at least one of the following:

[0025] Adjusting an indicator evaluation rule corresponding to a second target evaluation indicator in at least one evaluation indicator;

[0026] Add at least one supplementary evaluation indicator and an indicator evaluation rule corresponding to each supplementary evaluation indicator;

[0027] A third target evaluation indicator in at least one evaluation indicator is deleted.

[0028] According to an embodiment of the present disclosure, the quality assessment system is connected to an external device for a target port through an interface protocol;

[0029] Real-time collection of cargo data for target ports includes:

[0030] Collect data generated by external equipment in real time to obtain cargo data, wherein the external equipment may include at least one of the following: inspection equipment, testing equipment, monitoring equipment, and drone equipment;

[0031] The cargo data includes at least one of the following: basic cargo information, cargo picture information, cargo video information, cargo inspection information, cargo storage information, and cargo logistics information.

[0032] According to an embodiment of the present disclosure, the cargo type includes at least one of coal, copper concentrate, manganese ore, nickel ore, zinc ore, and iron ore.

[0033] Another aspect of the present disclosure provides a quality assessment system, comprising:

[0034] A data resource pool subsystem, used to collect cargo data for a target port from an external device in real time, and store the cargo data in a data resource pool, wherein the cargo data includes cargo data of at least one cargo type;

[0035] a quality risk assessment model subsystem, configured to establish at least one quality risk assessment model, wherein each quality risk assessment model corresponds to a type of goods; and

[0036] The risk analysis and early warning subsystem is used to call the target quality risk assessment model corresponding to the target cargo type in the quality risk assessment model subsystem for at least one target cargo type, and use the target quality risk assessment model to perform quality risk assessment on the cargo data of the target cargo type, and output the cargo quality assessment result for the target cargo type.

[0037] According to an embodiment of the present disclosure, the system further includes:

[0038] The quality risk display subsystem is used to push the cargo quality assessment results generated by the risk analysis and early warning subsystem to the preset terminal display device, and display them in real time and dynamically on the terminal device, so that users can view the details of the cargo quality assessment results and handle them on the terminal display device.

[0039] According to an embodiment of the present disclosure, the risk analysis and early warning subsystem includes:

[0040] A data interface module is used to call the cargo data corresponding to the target cargo type in the data resource pool;

[0041] A model calling module, used to call the target quality risk assessment model corresponding to the target cargo type in the quality risk assessment model subsystem;

[0042] A data analysis module, used to perform quality risk assessment on cargo data of a target cargo type using a target quality risk assessment model, and output a cargo quality assessment result for the target cargo type;

[0043] The risk conclusion storage module is used to store the cargo quality assessment results output by the data analysis module;

[0044] The analysis conclusion push module is used to determine the target terminal display device according to the preset evaluation result push path, and push the goods quality evaluation results to the target terminal display device.

[0045] According to an embodiment of the present disclosure, the quality risk display subsystem includes:

[0046] Risk conclusion publishing module, used to publish cargo quality assessment results;

[0047] A risk conclusion first display adaptation module, adapted to display the goods quality assessment result on a first type of terminal display device;

[0048] The risk conclusion second display adaptation module is used to adapt to display the goods quality assessment results on the second type of terminal display device.

[0049] Another aspect of the present disclosure provides an electronic device, comprising:

[0050] one or more processors;

[0051] a memory for storing one or more programs,

[0052] When the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0053] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0054] Another aspect of the present disclosure provides a computer program product, which includes computer executable instructions, and the instructions are used to implement the above method when executed.

[0055] According to the embodiments of the present disclosure, a technical means is adopted for real-time collection of cargo data for at least one cargo type at a target port; for a target cargo type in at least one cargo type, a target quality risk assessment model corresponding to the target cargo type is determined from at least one quality risk assessment model; and then a quality risk assessment is performed on the cargo data of the target cargo type using the target quality risk assessment model, and a cargo quality assessment result for the target cargo type is output. Since cargo data is collected in real time and analyzed and processed, the technical problem that manual inspection and supervision is difficult to cover the entire process and manual inspection efficiency is low, which easily leads to low port clearance efficiency, is at least partially overcome. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0057] Figure 1 An exemplary system architecture to which the quality assessment method according to an embodiment of the present disclosure can be applied is schematically shown;

[0058] Figure 2 A flowchart of a quality assessment method according to an embodiment of the present disclosure is schematically shown;

[0059] Figure 3 A schematic diagram of determining a cargo quality assessment result according to an embodiment of the present disclosure is schematically shown;

[0060] Figure 4 A block diagram of a quality assessment device according to an embodiment of the present disclosure is schematically shown;

[0061] Figure 5 A block diagram of a quality assessment system according to an embodiment of the present disclosure is schematically shown;

[0062] Figure 6 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0063] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0064] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0065] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0066] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0067] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security, and national security.

[0068] In the embodiments of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0069] The continuous and effective supply of bulk imports such as coal, copper concentrate, manganese ore, nickel ore, zinc ore, and iron ore can make up for the problem of resource shortage. However, with the continuous growth in the import volume of bulk goods, the problems of solid waste hidden in imported bulk goods and excessive harmful substances have brought challenges to energy security, economic security, and ecological security, as well as challenges to the supervision of bulk goods import business.

[0070] In order to prevent quality and safety risks such as solid waste inclusion and excessive harmful substances in imported bulk goods, product sampling and testing and manual inspection methods such as hand touch, visual inspection, and nose sniffing are mainly used. However, the sampling volume of bulk goods is large, the sample preparation requirements are complex, and multiple screenings are required. There are many inspectors and supporting testing instruments and equipment involved. The inspection process for each batch takes several days, which is time-consuming and labor-intensive, resulting in a long inspection and testing process and easily leading to low port clearance efficiency. In addition, manual inspection methods such as hand touch, visual inspection, and nose sniffing require the inspectors' rich professional knowledge, and it is difficult to meet the needs of on-site inspections when there is a shortage of inspection human resources.

[0071] In response to the above technical problems, an embodiment of the present disclosure provides a quality assessment method, which is applied to a quality assessment system, wherein the quality assessment system includes at least one quality risk assessment model, each quality risk assessment model corresponds to a type of goods, and the method includes: real-time collection of goods data for a target port, wherein the goods data includes goods data of at least one type of goods; for a target goods type in at least one type of goods, determining a target quality risk assessment model corresponding to the target goods type from at least one quality risk assessment model; and performing quality risk assessment on the goods data of the target goods type using the target quality risk assessment model, and outputting a goods quality assessment result for the target goods type.

[0072] Figure 1 The exemplary system architecture 100 to which the quality assessment method according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0073] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0074] The user may use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only for example).

[0075] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0076] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0077] It should be noted that the quality assessment method provided in the embodiment of the present disclosure can generally be performed by the server 105. Accordingly, the quality assessment system provided in the embodiment of the present disclosure can generally be arranged in the server 105. The quality assessment method provided in the embodiment of the present disclosure can also be performed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the quality assessment system provided in the embodiment of the present disclosure can also be arranged in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Alternatively, the quality assessment method provided in the embodiment of the present disclosure can also be performed by the first terminal device 101, the second terminal device 102 or the third terminal device 103, or can also be performed by other terminal devices that are different from the first terminal device 101, the second terminal device 102 or the third terminal device 103. Accordingly, the quality assessment system provided in the embodiment of the present disclosure may also be arranged in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0078] For example, the cargo data may be originally stored in any one of the first terminal device 101, the second terminal device 102, or the third terminal device 103 (for example, the first terminal device 101, but not limited thereto), or stored in an external storage device and imported into the first terminal device 101. Then, the first terminal device 101 may locally execute the quality assessment method provided by the embodiment of the present disclosure, or send the cargo data to other terminal devices, servers, or server clusters, and the other terminal devices, servers, or server clusters that receive the cargo data may execute the quality assessment method provided by the embodiment of the present disclosure.

[0079] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0080] Figure 2 The flowchart of the quality assessment method according to the embodiment of the present disclosure is schematically shown.

[0081] According to an embodiment of the present disclosure, the quality assessment method can be applied to a quality assessment system, where the quality assessment system includes at least one quality risk assessment model, and each quality risk assessment model corresponds to a type of goods.

[0082] For example, the quality assessment system may include a quality risk assessment model corresponding to coal, a quality risk assessment model corresponding to copper concentrate, a quality risk assessment model corresponding to manganese ore, a quality risk assessment model corresponding to nickel ore, a quality risk assessment model corresponding to zinc ore, and a quality risk assessment model corresponding to iron ore.

[0083] like Figure 2 As shown, the quality assessment method of the embodiment 200 includes operations S210 to S230.

[0084] In operation S210, cargo data for the target port is collected in real time, wherein the cargo data includes cargo data of at least one cargo type.

[0085] According to an embodiment of the present disclosure, the quality assessment system can be connected to an external device for a target port through an interface protocol; wherein the external device may include at least one of the following: inspection equipment, testing equipment, monitoring equipment, and drone equipment.

[0086] For example, real-time collection of cargo data for a target port may include: real-time collection of data generated by external devices to obtain cargo data, wherein the cargo data may include at least one of the following: basic cargo information, cargo picture information, cargo video information, cargo inspection information, cargo storage information, and cargo logistics information.

[0087] According to an embodiment of the present disclosure, data collection forms may include facilities and equipment, application systems, application program interfaces, applets, mobile applications, web pages, etc.

[0088] According to an embodiment of the present disclosure, the cargo type may include at least one of coal, copper concentrate, manganese ore, nickel ore, zinc ore, and iron ore.

[0089] According to an embodiment of the present disclosure, the collected cargo data for the target port may be stored in a database.

[0090] In operation S220, for a target cargo type among the at least one cargo type, a target quality risk assessment model corresponding to the target cargo type is determined from at least one quality risk assessment model.

[0091] According to an embodiment of the present disclosure, since each quality risk assessment model corresponds to a cargo type, after the cargo type is determined, a quality risk assessment model corresponding to the cargo type can be determined from at least one quality risk assessment model according to the cargo type.

[0092] For example, if the target cargo type is coal, a quality risk assessment model corresponding to coal can be determined from at least one quality risk assessment model to obtain a target quality risk assessment model.

[0093] For another example, if the target cargo type is iron ore, then a quality risk assessment model corresponding to the iron ore may be determined from at least one quality risk assessment model to obtain a target quality risk assessment model.

[0094] In operation S230, a quality risk assessment is performed on the cargo data of the target cargo type using the target quality risk assessment model, and a cargo quality assessment result for the target cargo type is output.

[0095] According to the embodiments of the present disclosure, a technical means is adopted for real-time collection of cargo data for at least one cargo type at a target port; for a target cargo type in at least one cargo type, a target quality risk assessment model corresponding to the target cargo type is determined from at least one quality risk assessment model; and then a quality risk assessment is performed on the cargo data of the target cargo type using the target quality risk assessment model, and a cargo quality assessment result for the target cargo type is output. Since cargo data is collected in real time and analyzed and processed, the technical problem that manual inspection and supervision is difficult to cover the entire process and manual inspection efficiency is low, which easily leads to low port clearance efficiency, is at least partially overcome.

[0096] According to an embodiment of the present disclosure, each quality risk assessment model may include at least one assessment indicator corresponding to the quality risk assessment model and an indicator assessment rule corresponding to each assessment indicator.

[0097] For example, the target quality risk assessment model may include at least one assessment indicator corresponding to the target quality risk assessment model and an indicator assessment rule corresponding to each assessment indicator. The at least one assessment indicator corresponding to the target quality risk assessment model and the indicator assessment rule corresponding to each assessment indicator are determined according to the type of goods corresponding to the target risk assessment model.

[0098] According to an embodiment of the present disclosure, the evaluation index may include, for example, cargo code, place of origin, place of declaration, import and export enterprise, cargo name, transportation time, transportation port of call, cargo price, and yard video. The index evaluation rule corresponding to the cargo code may be, for example, the first 4 digits include a preset array such as "2601", the index evaluation rule corresponding to the place of origin may be, for example, the place of origin is A or B, the index evaluation rule corresponding to the declaration place number may be, for example, the number is a preset number such as 04, the index evaluation rule corresponding to the import and export enterprise may be, for example, a non-dishonest enterprise, the index evaluation rule corresponding to the cargo name may be, for example, keywords such as pellet ore, pellet crushing, sintered ore, etc., the index evaluation rule corresponding to the transportation time may be, for example, not exceeding a preset time such as 60 days, the index evaluation rule corresponding to the transportation port of call may be, for example, not stopping at a non-target port, the index evaluation rule corresponding to the cargo price may be, for example, a preset percentage lower than the average price in a near preset time period, and the index evaluation rule corresponding to the yard video may be, for example, no abnormality.

[0099] According to an embodiment of the present disclosure, a target quality risk assessment model is used to perform a quality risk assessment on cargo data of a target cargo type, and outputting a cargo quality assessment result for the target cargo type includes: for an i-th evaluation indicator among at least one evaluation indicator, extracting an i-th indicator data corresponding to the i-th evaluation indicator from the cargo data of the target cargo type; using an i-th indicator evaluation rule corresponding to the i-th evaluation indicator, evaluating the i-th indicator data, and outputting an indicator evaluation result corresponding to the i-th evaluation indicator; and determining the cargo quality assessment result according to the indicator evaluation result of each evaluation indicator.

[0100] According to an embodiment of the present disclosure, the indicator evaluation result may include satisfying the indicator or not satisfying the indicator. For example, the i-th evaluation indicator is the cargo number, and the indicator evaluation rule corresponding to the i-th evaluation indicator is that the first 4 digits include "2601". If the cargo number includes "2601", the indicator evaluation result corresponding to the i-th evaluation indicator is satisfying the indicator; if the cargo number does not include "2601", the indicator evaluation result corresponding to the i-th evaluation indicator is not satisfying the indicator.

[0101] According to an embodiment of the present disclosure, determining the goods quality evaluation result according to the respective indicator evaluation result of each evaluation indicator includes: analyzing the respective indicator evaluation result of each evaluation indicator according to a preset quality evaluation rule to obtain the goods quality evaluation result.

[0102] According to an embodiment of the present disclosure, the cargo quality assessment result may be low risk, medium risk, or high risk.

[0103] The preset quality assessment rules may include: if all assessment indicators meet the indicator assessment rules, the risk level can be determined as low risk; if some assessment indicators meet the indicator assessment rules, the risk level can be determined as medium risk; if all assessment indicators do not meet the indicator assessment rules, the risk level can be determined as high risk.

[0104] In some embodiments, determining the goods quality assessment result according to the respective indicator assessment results of each assessment indicator may also include: scoring each indicator assessment result to obtain an assessment score for each indicator assessment result; then determining a total score according to the assessment score for each indicator assessment result, and determining the goods quality assessment result according to a preset risk level according to the total score. For example, if the total score is in the first interval, the risk level is low risk, if the total score is in the second interval, the risk level is medium risk, and if the total score is in the third interval, the risk level is high risk.

[0105] In some of the embodiments, at least one evaluation indicator may be highlighted in advance, and if the highlighted evaluation indicator does not satisfy the indicator evaluation rule, the risk level may be determined to be high risk.

[0106] Figure 3 A schematic diagram of determining a cargo quality assessment result according to an embodiment of the present disclosure is schematically shown.

[0107] like Figure 3As shown, first, a target quality risk assessment model is determined based on the acquired cargo data 310, wherein the target quality risk assessment model may, for example, include a first evaluation indicator 320 and a first indicator assessment rule 321 corresponding to the first evaluation indicator 320, a second evaluation indicator 330 and a second indicator assessment rule 331 corresponding to the second evaluation indicator 330, and a third evaluation indicator 340 and a third indicator assessment rule 341 corresponding to the third evaluation indicator 340. Then, for the first evaluation index 320, data related to the first evaluation index 320 is extracted from the goods data 310 to obtain the first index data 322; it is determined whether the first index data 322 satisfies the first index evaluation rule 321, thereby obtaining the first index evaluation result 323; for the second evaluation index 330, data related to the second evaluation index 330 is extracted from the goods data 310 to obtain the second index data 332; it is determined whether the second index data 332 satisfies the second index evaluation rule 331, thereby obtaining the second index evaluation result 333; for the third evaluation index 340, data related to the third evaluation index 340 is extracted from the goods data 310 to obtain the third index data 342; it is determined whether the third index data 342 satisfies the third index evaluation rule 341, thereby obtaining the third index evaluation result 343. The first index evaluation result 323, the second index evaluation result 333 and the third index evaluation result 343 are analyzed based on the quality evaluation rule to obtain the goods quality evaluation result 350.

[0108] According to an embodiment of the present disclosure, the above method also includes: determining a target terminal display device from at least one terminal display device according to a preset evaluation result push path; and pushing the cargo quality evaluation result to the target terminal display device.

[0109] According to an embodiment of the present disclosure, the evaluation result push path may include address information of the terminal display device, and may also include identification information of the terminal display device, for example, a number.

[0110] The evaluation result push path can be set according to actual needs in the push path configuration page of the quality evaluation system.

[0111] Terminal display devices may include large display screens in inspection halls, workstation display screens, mobile inspection terminals, etc.

[0112] According to an embodiment of the present disclosure, before pushing the goods quality assessment results to the target terminal display device, it also includes: adjusting the format of the goods quality assessment results according to the display method of the target terminal display device to obtain the adjusted goods quality assessment results; pushing the goods quality assessment results to the target terminal display device includes: sending the adjusted goods quality assessment results to the target terminal display device.

[0113] According to an embodiment of the present disclosure, the display method may include at least one of the following: pop-up display, voice broadcast prompt, text display, and scrolling display.

[0114] According to an embodiment of the present disclosure, the format adjustment may also include adjusting to a format adapted to a large screen or adjusting to a format of a video PC mobile terminal.

[0115] According to an embodiment of the present disclosure, the above method also includes: in response to an optimization operation on the target quality risk assessment model, generating an optimized target quality risk assessment model, wherein the optimization operation includes at least one of the following: adjusting the indicator evaluation rule corresponding to the second target evaluation indicator in at least one evaluation indicator; adding at least one supplementary evaluation indicator and the indicator evaluation rule corresponding to each supplementary evaluation indicator; deleting the third target evaluation indicator in at least one evaluation indicator.

[0116] According to an embodiment of the present disclosure, the optimization operation for the target quality risk assessment model may be determined based on the test results after testing the goods.

[0117] For example, for Goods A, after quality assessment using the quality risk assessment model corresponding to Goods A, the quality assessment result of Goods A is low risk. However, after experimental testing of Goods A, it is found that Goods A is high risk. At this time, the quality risk assessment model of Goods A needs to be optimized.

[0118] The specific optimization operation may include the optimization operation including adjusting the indicator evaluation rule corresponding to the second target evaluation indicator in at least one evaluation indicator; adding at least one supplementary evaluation indicator and the indicator evaluation rule corresponding to each supplementary evaluation indicator; deleting the third target evaluation indicator in at least one evaluation indicator.

[0119] Specifically, for example, the target quality risk assessment model includes assessment index a, assessment index b and assessment index c, as well as index assessment rule a1 corresponding to assessment index a, index assessment rule b1 corresponding to assessment index b and index assessment rule c1 corresponding to assessment index c. The optimization operation may include adjusting index assessment rule a1, and may also include deleting assessment index c; and may also include adding supplementary assessment index d and index assessment rule d1 corresponding to supplementary assessment index d.

[0120] According to the embodiments of the present disclosure, a bulk cargo quality risk assessment model is established based on a big data technology base through cargo data from multiple channels, quality risk assessment of imported bulk cargo is conducted, the quality and safety risk level of imported bulk cargo is analyzed and determined, and risk assessment conclusion information is output to guide port inspection personnel to implement differentiated inspection and supervision. This can solve the problems of insufficient port inspection personnel, difficulty in identifying solid waste inclusions, and long sampling and inspection time and high cost, save inspection and inspection human resources, shorten customs clearance logistics time, improve the port clearance efficiency of bulk cargo, and reduce customs clearance logistics costs. At the same time, it can improve the comprehensive environment and external service level of the port, and promote the sustainable development of the local export-oriented economy.

[0121] Figure 4 The block diagram schematically shows a quality assessment device according to an embodiment of the present disclosure.

[0122] According to an embodiment of the present disclosure, the quality assessment device can be applied to a quality assessment system, which includes at least one quality risk assessment model, and each quality risk assessment model corresponds to a type of goods.

[0123] like Figure 4 As shown, the quality assessment device of this embodiment 400 includes a data acquisition module 410 , a model determination module 420 and an assessment module 430 .

[0124] The data collection module 410 is used to collect cargo data for the target port in real time, wherein the cargo data includes cargo data of at least one type of cargo.

[0125] The model determination module 420 is used to determine, for a target cargo type in at least one cargo type, a target quality risk assessment model corresponding to the target cargo type from at least one quality risk assessment model.

[0126] The evaluation module 430 is used to perform quality risk evaluation on the cargo data of the target cargo type using the target quality risk evaluation model, and output the cargo quality evaluation result for the target cargo type.

[0127] According to an embodiment of the present disclosure, the target quality risk assessment model includes at least one assessment indicator and an indicator assessment rule corresponding to each assessment indicator.

[0128] According to an embodiment of the present disclosure, the evaluation module includes: an extraction submodule, an evaluation submodule and a determination submodule.

[0129] The extraction submodule is used to extract, for the i-th evaluation indicator among the at least one evaluation indicator, the i-th indicator data corresponding to the i-th evaluation indicator from the cargo data of the target cargo type.

[0130] The evaluation submodule is used to evaluate the ith indicator data using the ith indicator evaluation rule corresponding to the ith evaluation indicator, and output the indicator evaluation result corresponding to the ith evaluation indicator.

[0131] The determination submodule is used to determine the goods quality evaluation result according to the respective indicator evaluation result of each evaluation indicator.

[0132] According to an embodiment of the present disclosure, the determination submodule includes: an analysis unit.

[0133] The analysis unit is used to analyze the indicator evaluation results of each evaluation indicator according to the preset quality evaluation rules to obtain the goods quality evaluation results.

[0134] According to an embodiment of the present disclosure, the above-mentioned device further includes: a terminal determination module and a push module.

[0135] The terminal determination module determines the target terminal display device from at least one terminal display device according to the preset evaluation result push path;

[0136] The push module is used to push the goods quality assessment results to the target terminal display device.

[0137] According to an embodiment of the present disclosure, the above-mentioned device also includes: a format adjustment module.

[0138] A format adjustment module, used to adjust the format of the goods quality assessment result according to the display mode of the target terminal display device to obtain the adjusted goods quality assessment result;

[0139] The push module is also used to send the adjusted goods quality assessment results to the target terminal display device.

[0140] According to an embodiment of the present disclosure, the above-mentioned device also includes: an optimization module.

[0141] An optimization module is used to generate an optimized target quality risk assessment model in response to an optimization operation on the target quality risk assessment model, wherein the optimization operation includes at least one of the following: adjusting the indicator evaluation rule corresponding to the second target evaluation indicator in at least one evaluation indicator; adding at least one supplementary evaluation indicator and the indicator evaluation rule corresponding to each supplementary evaluation indicator; deleting the third target evaluation indicator in at least one evaluation indicator.

[0142] According to the embodiments of the present invention, any one or more of the modules, sub-modules, and units, or at least part of the functions of any one of them, can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, sub-modules, and units can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, sub-modules, and units can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application-specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, sub-modules, and units can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0143] For example, any multiple of the data acquisition module 410, the model determination module 420, and the evaluation module 430 can be combined in one module / unit / sub-unit for implementation, or any one of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the data acquisition module 410, the model determination module 420, and the evaluation module 430 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the data collection module 410 , the model determination module 420 , and the evaluation module 430 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be performed.

[0144] It should be noted that the quality assessment device part in the embodiment of the present disclosure corresponds to the quality assessment method part in the embodiment of the present disclosure. The description of the quality assessment device part specifically refers to the quality assessment method part, which will not be repeated here.

[0145] Figure 5 The block diagram of the quality assessment system according to the embodiment of the present disclosure is schematically shown.

[0146] like Figure 5 As shown, the quality assessment system 500 includes a data resource pool subsystem 510 , a quality risk assessment model subsystem 520 , and a risk analysis and early warning subsystem 530 .

[0147] The data resource pool subsystem 510 is used to collect cargo data for the target port from an external device in real time and store the cargo data in a data resource pool, wherein the cargo data includes cargo data of at least one cargo type. It should be noted that the data resource pool subsystem 510 may correspond to the above-mentioned data collection module 410.

[0148] The quality risk assessment model subsystem 520 is used to establish at least one quality risk assessment model, wherein each quality risk assessment model corresponds to one type of goods.

[0149] The risk analysis and early warning subsystem 530 is used to call the target quality risk assessment model corresponding to the target cargo type in the quality risk assessment model subsystem for at least one cargo type, and use the target quality risk assessment model to perform quality risk assessment on cargo data of the target cargo type, and output the cargo quality assessment result for the target cargo type. It should be noted that the risk analysis and early warning subsystem 530 may correspond to the above-mentioned model determination module 430 and assessment module 430.

[0150] According to an embodiment of the present disclosure, the data resource pool subsystem 510 includes a data interface module, a data receiving module, a data parsing module and a data storage module.

[0151] The data interface module is used to connect data with external devices through the interface protocol, and automatically collect cargo data for the target port, such as manifest data, customs declaration data, transport vehicle dynamic data, inspection instruction data, forklift operation data, bulk cargo unloading data, bulk cargo radiation detection data, bulk cargo solid waste screening data, bulk cargo storage data, vehicle pickup data, import and export enterprise data, GIS data, video images, etc.

[0152] The data receiving module is used to receive the cargo data collected by the data interface module.

[0153] The data analysis module is used to analyze the cargo data received by the data receiving module.

[0154] The data storage module is used to locally store the cargo data analyzed by the data analysis module to form a data resource pool.

[0155] According to an embodiment of the present disclosure, the quality risk assessment model subsystem 520 includes an assessment model building module, an assessment model optimization module, an assessment model maintenance module and a model calling interface module.

[0156] The assessment model establishment module is used to establish a quality risk assessment model for bulk goods, and supports application model establishment, algorithm model establishment, business parameter maintenance, etc. Among them, application module establishment includes establishing quality risk assessment models for different types of goods; algorithm model establishment can include establishing different assessment indicators and indicator assessment rules.

[0157] The assessment model optimization module is used to optimize the risk assessment application model and algorithm model based on the analysis of data related to the quality and safety risks of bulk cargo imports, and continuously improve the quality risk assessment capabilities of the risk assessment application model and algorithm model.

[0158] The assessment model maintenance module is used to enable, disable, modify parameters, modify algorithms, etc. of the risk assessment model.

[0159] The model calling interface module is used to provide various quality risk assessment models for the risk analysis and early warning subsystem.

[0160] According to an embodiment of the present disclosure, the risk analysis and early warning subsystem 530 includes: a data interface module, a model calling module, a data analysis module, a risk conclusion storage module and an analysis conclusion push module.

[0161] A data interface module is used to call the cargo data corresponding to the target cargo type in the data resource pool;

[0162] A model calling module is used to call the target quality risk assessment model corresponding to the target cargo type in the quality risk assessment model subsystem, and the data related to the cargo in the data resource pool;

[0163] The data analysis module is used to use the target quality risk assessment model to perform quality risk assessment on the cargo data of the target cargo type, and output the cargo quality assessment result for the target cargo type. It should be noted that the data analysis module corresponds to the above-mentioned assessment module. Similarly, the data analysis module can also include: an extraction submodule, an assessment submodule and a determination submodule.

[0164] The risk conclusion storage module is used to store the cargo quality assessment results output by the data analysis module;

[0165] The analysis conclusion push module is used to determine the target terminal display device according to the preset evaluation result push path, and push the goods quality evaluation result to the target terminal display device. It should be noted that the analysis conclusion push module corresponds to the above-mentioned terminal determination module and push module.

[0166] According to an embodiment of the present disclosure, the above-mentioned system 500 also includes: a quality risk display subsystem 540, which is used to push the cargo quality assessment results generated by the risk analysis and early warning subsystem to a preset terminal display device, and to display them in real time and dynamically on the terminal device, so that users can view the details and handle the cargo quality assessment results on the terminal display device.

[0167] According to an embodiment of the present disclosure, the quality risk display subsystem 540 includes: a risk conclusion publishing module, a risk conclusion first display adaptation module, and a risk conclusion second display adaptation module.

[0168] Risk conclusion publishing module, used to publish cargo quality assessment results;

[0169] The risk conclusion first display adaptation module is used to adapt to display the goods quality assessment result on the first type of terminal display device, wherein the first type of terminal display device can be a large-screen terminal device; it should be noted that the risk conclusion first display adaptation module can be used with Figure 5 The risk conclusion in the large-screen display adaptation module corresponds.

[0170] The risk conclusion second display adaptation module is used to adapt to display the goods quality assessment results on the second type of terminal display device, wherein the second type of terminal display device can be a PC (Personal Computer) terminal device. It should be noted that the risk conclusion second display adaptation module can be used with Figure 5 The risk conclusion in the PC display adaptation module corresponds.

[0171] Through the integration of multiple functionally independent subsystems, the quality assessment system can realize the collection, processing and storage of big data related to the customs clearance and logistics links of bulk goods. The bulk goods quality risk assessment model can be used to realize intelligent analysis of the quality risks of imported bulk goods. The risk analysis conclusions are automatically pushed to external devices such as large screens, workstations and mobile terminals for risk control personnel and inspection personnel to view and handle operations. It can solve the problems of limited port inspection human resources, long customs clearance time for bulk goods imports, high customs clearance logistics costs, and low port clearance efficiency, save inspection human resources, shorten the customs clearance time for bulk goods imports, and simplify the inspection process for bulk goods.

[0172] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0173] Figure 6 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0174] like Figure 6 As shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 to a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0175] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0176] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0177] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0178] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0179] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0180] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 602 and / or the RAM 603 described above and / or one or more memories other than the ROM 602 and the RAM 603 .

[0181] The embodiments of the present disclosure also include a computer program product, which includes a computer program, and the computer program contains program code for executing the method provided by the embodiments of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the method provided by the embodiments of the present disclosure.

[0182] When the computer program is executed by the processor 601, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0183] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0184] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0185] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0186] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A quality assessment method, applied to a quality assessment system, wherein the quality assessment system includes at least one quality risk assessment model, each of which corresponds to a type of goods, and the method includes: Collect cargo data for a target port in real time, wherein the cargo data includes cargo data of at least one cargo type; For a target cargo type among at least one cargo type, determining a target quality risk assessment model corresponding to the target cargo type from the at least one quality risk assessment model; and The target quality risk assessment model is used to perform quality risk assessment on the cargo data of the target cargo type, and a cargo quality assessment result for the target cargo type is output.

2. The method according to claim 1, wherein: The target quality risk assessment model includes at least one assessment indicator and an indicator assessment rule corresponding to each assessment indicator; The using the target quality risk assessment model to perform quality risk assessment on the cargo data of the target cargo type, and outputting the cargo quality assessment result for the target cargo type comprises: For an i-th evaluation indicator among the at least one evaluation indicator, extracting i-th indicator data corresponding to the i-th evaluation indicator from the cargo data of the target cargo type; Using the i-th indicator evaluation rule corresponding to the i-th evaluation indicator, evaluate the i-th indicator data, and output the indicator evaluation result corresponding to the i-th evaluation indicator; The goods quality evaluation result is determined according to the respective indicator evaluation result of each of the evaluation indicators.

3. The method according to claim 2, wherein: The goods quality assessment result is determined according to the respective assessment results of each assessment indicator, including: According to the preset quality assessment rules, the indicator assessment results of each of the assessment indicators are analyzed to obtain the goods quality assessment results.

4. The method according to claim 1, further comprising: Determine a target terminal display device from at least one terminal display device according to a preset evaluation result push path; Push the cargo quality assessment result to the target terminal display device.

5. The method according to claim 4, before pushing the goods quality assessment result to the target terminal display device, further comprising: Adjusting the format of the goods quality assessment result according to the display mode of the target terminal display device to obtain an adjusted goods quality assessment result; The pushing of the goods quality assessment result to the target terminal display device comprises: The adjusted goods quality assessment result is sent to the target terminal display device.

6. The method according to claim 5, wherein: The display method may include at least one of the following: pop-up display, voice broadcast prompt, text display, and scrolling display.

7. The method according to claim 2, further comprising: In response to an optimization operation on the target quality risk assessment model, an optimized target quality risk assessment model is generated, wherein the optimization operation includes at least one of the following: Adjusting an indicator evaluation rule corresponding to a second target evaluation indicator in at least one evaluation indicator; Add at least one supplementary evaluation indicator and an indicator evaluation rule corresponding to each supplementary evaluation indicator; A third target evaluation indicator in at least one evaluation indicator is deleted.

8. The method according to claim 1, wherein: The quality assessment system is connected to an external device for the target port through an interface protocol; The real-time collection of cargo data for the target port includes: Collect data generated by the external device in real time to obtain the cargo data, wherein the external device may include at least one of the following: inspection equipment, testing equipment, monitoring equipment, and drone equipment; The cargo data includes at least one of the following: basic cargo information, cargo picture information, cargo video information, cargo inspection information, cargo storage information, and cargo logistics information.

9. The method according to claim 1, wherein: The cargo types include at least one of coal, copper concentrate, manganese ore, nickel ore, zinc ore and iron ore.

10. A quality assessment system comprising: A data resource pool subsystem, used for collecting cargo data for a target port from an external device in real time, and storing the cargo data in a data resource pool, wherein the cargo data includes cargo data of at least one cargo type; a quality risk assessment model subsystem, configured to establish at least one quality risk assessment model, wherein each quality risk assessment model corresponds to one type of goods; and The risk analysis and early warning subsystem is used to call the target quality risk assessment model corresponding to the target cargo type in the quality risk assessment model subsystem for at least one cargo type, and use the target quality risk assessment model to perform quality risk assessment on the cargo data of the target cargo type, and output the cargo quality assessment result for the target cargo type.

11. The system according to claim 10, further comprising: The quality risk display subsystem is used to push the cargo quality assessment results generated by the risk analysis and early warning subsystem to a preset terminal display device, and to display them in real time and dynamically on the terminal device, so that the user can view the details of the cargo quality assessment results and perform handling operations on the terminal display device.

12. The system according to claim 10, wherein: The risk analysis and early warning subsystem includes: A data interface module, used for calling the cargo data corresponding to the target cargo type in the data resource pool; A model calling module, used for calling the target quality risk assessment model corresponding to the target cargo type in the quality risk assessment model subsystem; A data analysis module, configured to perform a quality risk assessment on the cargo data of the target cargo type using the target quality risk assessment model, and output a cargo quality assessment result for the target cargo type; A risk conclusion storage module, used to store the cargo quality assessment results output by the data analysis module; The analysis conclusion push module is used to determine the target terminal display device according to the preset evaluation result push path, and push the goods quality evaluation result to the target terminal display device.

13. The system according to claim 11, wherein: The quality risk display subsystem includes: A risk conclusion publishing module, used to publish the cargo quality assessment results; A risk conclusion first display adaptation module, adapted to display the goods quality assessment result on a first type of terminal display device; The risk conclusion second display adaptation module is used to adapt to display the goods quality assessment result on a second type of terminal display device.

14. An electronic device comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 9.

15. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 9.

16. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 9.

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