Commodity supply chain visualization supervision system and supervision method

Abstract

The present invention discloses a commodity supply chain visualization supervision system and a supervision method thereof, and belongs to the technical field of supply chain supervision. The system is used to solve the technical problems of poor supervision effect and poor autonomous optimization management effect in commodity supply chain information processing in existing solutions. The monitoring data corresponding to each information processing of different equipment targets in the commodity supply chain is processed and calculated to obtain application processing degrees and single request supervision data corresponding to different application request objects. First, all single request supervision data corresponding to different equipment targets performing information processing in the commodity supply chain are screened for effectiveness, and local operation status analysis of different dimensions is performed on different equipment targets. Further, data calculation is performed on the local operation status analysis results of different dimensions, and data analysis is performed on the overall operation status values ​​corresponding to different equipment targets, and dynamic optimization management is performed on the information processing corresponding to different equipment targets.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain supervision, and in particular to a commodity supply chain visualization supervision system and a supervision method thereof. Background Art

[0002] The commodity supply chain refers to all steps and processes from the acquisition of raw materials to the delivery of the final product to the hands of consumers, covering everything from the initial production to the consumption of the commodity, including but not limited to the procurement of raw materials, product manufacturing, assembly, distribution, storage, processing and final sales and services.

[0003] When implementing the existing commodity supply chain supervision scheme, it is unable to supervise and evaluate each information processing of the commodity supply chain from different aspects, and to adaptively and dynamically optimize the information processing of the commodity supply chain according to the processing results of different aspects to ensure the efficiency and processing effect of information processing. There are technical problems such as poor supervision effect and poor autonomous optimization management effect in commodity supply chain information processing. Summary of the invention

[0004] The purpose of the present invention is to provide a commodity supply chain visualization supervision system and a supervision method thereof, which are used to solve the technical problems that the existing solutions cannot supervise and evaluate the information processing of the commodity supply chain from different aspects and adaptively perform dynamic optimization management, resulting in poor supervision effect on commodity supply chain information processing and poor autonomous optimization management effect.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] The commodity supply chain visual supervision system includes:

[0007] The local supervision module of commodity supply chain information processing targets is used to obtain all equipment targets for information processing in the commodity supply chain and implement different aspects of operation data monitoring and statistics, and digitally process the operation data monitored and counted in different aspects to obtain the operation supervision processing sequence corresponding to the equipment targets;

[0008] The overall supervision module of commodity supply chain information processing targets is used to screen the validity of all single request supervision data corresponding to different equipment targets performing information processing in the commodity supply chain, obtain the request monitoring and screening data sets corresponding to different equipment targets, perform local operation status analysis and overall operation status analysis of the equipment targets in different dimensions based on the request monitoring and screening data sets, and dynamically optimize the information processing corresponding to different equipment targets based on the analysis results;

[0009] Among them, all single request supervision data corresponding to different device targets are screened for validity, and request processing identifiers corresponding to different single request supervision data of device targets are obtained;

[0010] According to the request processing identifier with a value of 1, the single request supervision data is marked as valid screening data, and all valid screening data corresponding to the device target are sorted and combined to obtain the request monitoring screening data set corresponding to the device target;

[0011] Perform local operation status processing of self-requests and local operation status processing of collaborative requests on valid screening data corresponding to all self-request types and all collaborative request types in the request monitoring screening data set, respectively, to obtain first local operation status degrees corresponding to all self-request types and second local operation status degrees corresponding to all collaborative request types;

[0012] Processing the first local operating status degree and the second local operating status degree obtained by calculation to obtain an overall operating status identifier corresponding to the device target;

[0013] Targeted information processing optimization management is implemented for different equipment targets based on the overall operating status identification obtained through processing.

[0014] Preferably, all equipment targets for information processing in the commodity supply chain are obtained and numbered according to the supply sequence of the commodity supply chain, and the supply link to which the equipment targets belong is obtained at the same time;

[0015] When implementing different aspects of operation data monitoring and statistics for all equipment targets in the commodity supply chain, when an application instruction is monitored, the equipment target to which the application instruction belongs is marked as a selected equipment target, and the application timestamp corresponding to the application instruction is obtained. At the same time, the completion timestamp corresponding to the application content of the application instruction when the selected equipment target completes the application is monitored; the time difference between the completion timestamp and the application timestamp is calculated and marked as the single application processing time difference.

[0016] Preferably, the application request object and application content corresponding to the application instruction are input into the request database for traversal retrieval to obtain the standard application processing time difference corresponding to the application instruction, and the application processing degree SC of the application instruction corresponding to the selected device target is calculated by the formula SC=SQ×Tc / Tb; wherein SQ is the request object weight corresponding to the application request object; Tc is the single application processing time difference; Tb is the standard application processing time difference;

[0017] The application timestamp, completion timestamp, single application processing time difference and application processing degree corresponding to the application instruction are sorted and combined to obtain single request supervision data;

[0018] The request type of the application instruction corresponding to the selected device target is obtained according to the application content, and the single request supervision data is stored in the request type data table associated with the corresponding selected device target according to the obtained request type.

[0019] Preferably, when performing validity screening on all single request supervision data corresponding to different device targets, the application processing degrees in all single request supervision data of the device target are respectively input into the request supervision identification piecewise function for data analysis and outputting the corresponding request processing identifier;

[0020] Among them, the expression of the request supervision identification piecewise function is ; In the formula, SC0 is the application processing standard value of the device target corresponding to the application instruction;

[0021] The request processing identifier contains a value of 0 or 1. A request processing identifier with a value of 0 indicates that the corresponding single request processing status is normal, and a request processing identifier with a value of 1 indicates that the corresponding single request processing status is abnormal;

[0022] According to the request processing identifier with a value of 1, the single request supervision data is marked as valid screening data, and all valid screening data corresponding to the device target are sorted and combined to obtain the request monitoring screening data set corresponding to the device target.

[0023] Preferably, when performing self-request local operation status processing analysis and collaborative request local operation status processing analysis on the valid screening data corresponding to all self-request types and all collaborative request types in the request monitoring screening data set, respectively, the application processing degree in the valid screening data corresponding to all self-request types is obtained and marked as SCi, i={1, 2, 3, ..., n}, n is a positive integer; i is the valid screening data corresponding to all self-request types; and the application processing degree in the valid screening data corresponding to all collaborative request types is obtained and marked as SCj, j={1, 2, 3, ..., m}, m is a positive integer; j is the valid screening data corresponding to all collaborative request types.

[0024] Preferably, the first operating state processing formula Calculate and obtain the first local operation status YZy corresponding to all self-request types; where SCi0 is the application processing standard value corresponding to the effective screening data of different self-request types; A is the self-request integration standard value; N1 is the total number of application requests of all self-request types processed by the device target; N is the total number of all application requests processed by the device target;

[0025] And, through the second operating state processing formula Calculate and obtain the second local operating state degree EZy corresponding to all collaborative request types; where SCj0 is the application processing standard value corresponding to different effective screening data of collaborative request types; B is the collaborative request integration standard value; N2 is the total number of application requests for all collaborative request types processed by the device target.

[0026] Preferably, the first local operating state degree and the second local operating state degree obtained by calculating the equipment target are analyzed by integrating the operating state identification piecewise function to obtain the overall operating state identifier corresponding to the equipment target;

[0027] Among them, the expression of the integrated running state identification piecewise function is: ; In the formula, ZY is the overall operating status identification; α, β are different weight coefficients, and α+β=1; C is the overall operating status standard value;

[0028] The overall operation status identifier includes a value of 0 or 1. The overall operation status identifier with a value of 0 indicates that the information processing status corresponding to the device target is normal, and the overall operation status identifier with a value of 1 indicates that the information processing status corresponding to the device target is abnormal.

[0029] Preferably, the device target corresponding to the overall operation status identifier with a value of 1 is marked as a selected device target, and targeted information processing optimization management is implemented for all selected device targets.

[0030] Visual supervision methods for commodity supply chains include:

[0031] Obtain all equipment targets for information processing in the commodity supply chain and implement different aspects of operation data monitoring and statistics, and digitally process the operation data monitored and counted in different aspects to obtain the operation supervision processing sequence corresponding to the equipment targets;

[0032] All single request supervision data corresponding to different equipment targets performing information processing in the commodity supply chain are screened for validity, and request monitoring and screening data sets corresponding to different equipment targets are obtained. Based on the request monitoring and screening data sets, local operation status analysis and overall operation status analysis are performed on the equipment targets in different dimensions, and information processing corresponding to different equipment targets is dynamically optimized and managed based on the analysis results.

[0033] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0034] The present invention processes and calculates the monitoring data corresponding to each information processing of different equipment targets in the commodity supply chain, obtains the application processing degree corresponding to different application request objects, realizes the digital processing of each application request of different equipment targets, and can also provide reliable local digital analysis data support for the subsequent different aspects of the operation status analysis corresponding to different equipment targets. The present invention first screens the effectiveness of all single request supervision data corresponding to different equipment targets that perform information processing in the commodity supply chain, and implements local operation status analysis of different dimensions for different equipment targets based on the screening results, and further calculates the data of the local operation status analysis results of different dimensions to obtain the overall operation status values ​​corresponding to different equipment targets, performs data analysis on the overall operation status values ​​corresponding to different equipment targets, and dynamically optimizes the information processing corresponding to different equipment targets, thereby improving the supervision effect and autonomous optimization management effect in terms of information processing in the commodity supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 This is a module block diagram of the commodity supply chain visualization supervision system of the present invention.

[0037] Figure 2 It is a flowchart of the effectiveness screening of all single request supervision data corresponding to different device targets in the present invention.

[0038] Figure 3 It is a flowchart of the commodity supply chain visualization supervision method of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0040] Example 1: Figure 1 As shown, the present invention is a commodity supply chain visualization supervision system, including a commodity supply chain information processing target local supervision module and a commodity supply chain information processing target overall supervision module;

[0041] The local supervision module of commodity supply chain information processing targets is used to obtain all equipment targets for information processing in the commodity supply chain and implement different aspects of operation data monitoring and statistics, and digitally process the operation data monitored and counted in different aspects to obtain the operation supervision processing sequence corresponding to the equipment targets; including:

[0042] According to the supply sequence of the commodity supply chain, all equipment targets for information processing in the commodity supply chain are obtained and numbered, and at the same time, the supply link to which the equipment targets belong is obtained;

[0043] It should be noted that the equipment targets for information processing include but are not limited to mobile terminals and desktop computers; the supply links include but are not limited to procurement management, order processing, inventory management, logistics management, sales management and after-sales service;

[0044] When monitoring and collecting statistics of different aspects of operation data of all equipment targets in the commodity supply chain, when an application instruction is detected, the equipment target to which the application instruction belongs is marked as the selected equipment target, and the application timestamp corresponding to the application instruction is obtained. At the same time, the completion timestamp corresponding to the application content of the application instruction completed by the selected equipment target is monitored and obtained; the time difference between the completion timestamp and the application timestamp is calculated and marked as the single application processing time difference;

[0045] Among them, the monitoring of application instructions can be realized based on the existing sensor recognition technology and software recognition technology; the generation of application instructions is generated by the staff actively clicking on the equipment target;

[0046] Specifically, sensor recognition technology: usually includes multiple sensors, such as accelerometers, gyroscopes, etc. These sensors can help monitor user actions, including click operations;

[0047] Software recognition technology: operating system-level event monitoring mechanism, such as event listeners in Android or iOS, can capture touch events and process them accordingly;

[0048] Click event processing within the application. Developers can set up listeners within the application to capture and process click events;

[0049] The units corresponding to different timestamps and the time difference of a single application processing are all seconds;

[0050] The application request object and application content corresponding to the application instruction are input into the request database for traversal retrieval to obtain the standard application processing time difference corresponding to the application instruction, and the application processing degree SC corresponding to the application instruction of the selected device target is calculated by the formula SC=SQ×Tc / Tb; where SQ is the request object weight corresponding to the application request object, which can be determined according to the total number of all application requests in the history corresponding to the application request object; Tc is the single application processing time difference; Tb is the standard application processing time difference;

[0051] It is worth noting that since different application request objects have different request object weights, the corresponding application requests have different impacts. By adding the data calculation of the request object weight, the adaptability and reliability of the data calculation corresponding to the application processing degree can be effectively improved;

[0052] It should be noted that the application request object is determined according to the operation function options corresponding to all equipment targets included in the existing different supply links. Different operation function options are associated with several request contents, which can be specifically determined according to the operation design data corresponding to the actual equipment target; for example, the application request object is specifically the A operation function option in the equipment target, and the corresponding request content includes the B operation function corresponding to the requested equipment target, or includes the C operation function corresponding to the equipment targets of different supply links;

[0053] In addition, the request database pre-stores a number of sample application request objects and a number of corresponding sample application contents and standard application processing time differences, and different sample application request objects and a number of corresponding sample application contents and standard application processing time differences can be determined according to the operation design data corresponding to all equipment targets in different supply links of the commodity supply chain;

[0054] The application timestamp, completion timestamp, single application processing time difference and application processing degree corresponding to the application instruction are sorted and combined to obtain single request supervision data; the request type of the application instruction corresponding to the selected device target is obtained according to the application content, and the single request supervision data is stored in the request type data table associated with the corresponding selected device target according to the obtained request type; wherein the request type includes the own request type and the collaborative request type;

[0055] In the embodiment of the present invention, by processing and calculating the monitoring data corresponding to each information processing of different equipment targets in the commodity supply chain, the application processing degree corresponding to different application request objects is obtained, so that the digital processing of each application request of different equipment targets is realized, and reliable local digital analysis data support can be provided for the subsequent operation status analysis of different aspects corresponding to different equipment targets;

[0056] The overall supervision module of commodity supply chain information processing targets is used to screen the validity of all single request supervision data corresponding to different equipment targets for information processing in the commodity supply chain, obtain the request monitoring and screening data sets corresponding to different equipment targets, perform local operation status analysis and overall operation status analysis of the equipment targets in different dimensions according to the request monitoring and screening data sets, and dynamically optimize the information processing corresponding to different equipment targets according to the analysis results; including:

[0057] like Figure 2As shown, when all single request supervision data corresponding to different equipment targets are screened for validity, the application processing degrees in all single request supervision data of the equipment targets are respectively input into the request supervision identification piecewise function for data analysis and the corresponding request processing identifier is output;

[0058] Among them, the expression of the request supervision identification piecewise function is ; Wherein, SC0 is the application processing standard value of the device target corresponding to the application instruction, which can be determined according to the application request object of the device target and the operation design data corresponding to the application content;

[0059] The request processing identifier contains a value of 0 or 1. A request processing identifier with a value of 0 indicates that the corresponding single request processing status is normal, and a request processing identifier with a value of 1 indicates that the corresponding single request processing status is abnormal;

[0060] It should be noted that the request processing identifier is a digital representation used to filter the single request processing status of the device target;

[0061] In the embodiment of the present invention, by digitally processing the single request monitoring data corresponding to different device targets and performing validity screening, the single request processing status corresponding to different device targets can be obtained, thereby providing reliable screening data support for subsequent operation status analysis corresponding to different aspects of different device targets;

[0062] According to the request processing identifier with a value of 1, the single request supervision data is marked as valid screening data, and all valid screening data corresponding to the device target are sorted and combined to obtain the request monitoring screening data set corresponding to the device target;

[0063] When performing self-request local operation status processing analysis and collaborative request local operation status processing analysis on the valid screening data corresponding to all self-request types and all collaborative request types in the request monitoring screening data set, respectively, the application processing degree in the valid screening data corresponding to all self-request types is obtained and marked as SCi, i={1, 2, 3, ..., n}, n is a positive integer; i is the valid screening data corresponding to all self-request types; and the application processing degree in the valid screening data corresponding to all collaborative request types is obtained and marked as SCj, j={1, 2, 3, ..., m}, m is a positive integer; j is the valid screening data corresponding to all collaborative request types;

[0064] Processing formula through the first running state Calculate and obtain the first local operation state degree YZy corresponding to all self-request types; where SCi0 is the application processing standard value corresponding to the effective screening data of different self-request types; A is the self-request integration standard value, which is determined according to the operation design data of the equipment target; N1 is the total number of application requests of all self-request types processed by the equipment target; N is the total number of all application requests processed by the equipment target;

[0065] And, through the second operating state processing formula Calculate and obtain the second local operation state degree EZy corresponding to all collaborative request types; where SCj0 is the application processing standard value corresponding to different effective screening data of collaborative request types; B is the collaborative request integration standard value, which is determined according to the operation design data of the equipment target; N2 is the total number of application requests of all collaborative request types processed by the equipment target;

[0066] In the embodiment of the present invention, by calculating the data of the local operating status corresponding to different device targets from the dimension of own request type and the dimension of collaborative request type respectively, the local operating status degree of the corresponding dimension is obtained, so that the data integration calculation and digital representation of different device targets in different dimensions are realized, and digital data support of different dimensions can also be provided for the subsequent overall operating status processing and analysis of different devices;

[0067] The first local operating state degree and the second local operating state degree obtained by calculating the equipment target are analyzed by integrating the operating state identification piecewise function to obtain the overall operating state identifier corresponding to the equipment target;

[0068] Among them, the expression of the integrated running state identification piecewise function is: ; In the formula, ZY is the overall operating status identification; α and β are different weight coefficients, and α+β=1. The specific values ​​of α and β are not limited and can be customized according to the application requirements of the actual application scenario; C is the overall operating status standard value, which is determined according to the operating design data of the equipment target;

[0069] It should be noted that the overall operation status identification is used to integrate and calculate all local operation status supervision data of the equipment target in different dimensions to digitally represent its overall operation status;

[0070] The overall operation status flag includes a value of 0 or 1. The overall operation status flag with a value of 0 indicates that the information processing status corresponding to the device target is normal, and the overall operation status flag with a value of 1 indicates that the information processing status corresponding to the device target is abnormal;

[0071] Mark the device target corresponding to the overall operation status indicator with a value of 1 as a selected device target, and implement targeted information processing optimization management for all selected device targets;

[0072] Among them, implementing targeted information processing optimization management for all selected equipment targets, including but not limited to hardware upgrade management and network upgrade management of selected equipment targets, can effectively improve the overall internal processing efficiency and overall collaborative processing efficiency of information processing for all equipment targets in the commodity supply chain.

[0073] In an embodiment of the present invention, all single-request supervision data corresponding to different device targets performing information processing in a commodity supply chain are first screened for validity, and local operating status analysis of different dimensions is performed on different device targets based on the screening results. The local operating status analysis results of different dimensions are further subjected to data calculation to obtain overall operating status values ​​corresponding to different device targets. Data analysis is performed on the overall operating status values ​​corresponding to different device targets, and information processing corresponding to different device targets is dynamically optimized and managed, thereby improving the supervision effect and autonomous optimization management effect on information processing in the commodity supply chain.

[0074] Example 2: Figure 3 As shown, the present invention is a method for visually supervising a commodity supply chain, comprising:

[0075] Obtain all equipment targets for information processing in the commodity supply chain and implement different aspects of operation data monitoring and statistics, and digitally process the operation data monitored and counted in different aspects to obtain the operation supervision processing sequence corresponding to the equipment targets;

[0076] All single request supervision data corresponding to different equipment targets performing information processing in the commodity supply chain are screened for validity, and request monitoring and screening data sets corresponding to different equipment targets are obtained. Based on the request monitoring and screening data sets, local operation status analysis and overall operation status analysis are performed on the equipment targets in different dimensions, and information processing corresponding to different equipment targets is dynamically optimized and managed based on the analysis results.

[0077] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0078] In the several embodiments provided by the present invention, it should be understood that the disclosed system and its supervision method can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0079] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0081] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. Commodity supply chain visualization supervision system, characterized by: include: The local supervision module of commodity supply chain information processing targets is used to obtain all equipment targets for information processing in the commodity supply chain and implement different aspects of operation data monitoring and statistics, and digitally process the operation data monitored and counted in different aspects to obtain the operation supervision processing sequence corresponding to the equipment targets; Among them, all equipment targets for information processing in the commodity supply chain are obtained and numbered according to the supply sequence of the commodity supply chain, and the supply link to which the equipment targets belong is obtained at the same time; When monitoring and collecting statistics of different aspects of operation data of all equipment targets in the commodity supply chain, when an application instruction is detected, the equipment target to which the application instruction belongs is marked as the selected equipment target, and the application timestamp corresponding to the application instruction is obtained. At the same time, the completion timestamp corresponding to the application content of the application instruction completed by the selected equipment target is monitored and obtained; the time difference between the completion timestamp and the application timestamp is calculated and marked as the single application processing time difference; The application request object and application content corresponding to the application instruction are input into the request database for traversal retrieval to obtain the standard application processing time difference corresponding to the application instruction, and the application processing degree SC of the application instruction corresponding to the selected equipment target is calculated by the formula SC=SQ×Tc / Tb; where SQ is the request object weight corresponding to the application request object; Tc is the single application processing time difference; Tb is the standard application processing time difference; The application timestamp, completion timestamp, single application processing time difference and application processing degree corresponding to the application instruction are sorted and combined to obtain single request supervision data; Obtain the request type of the application instruction corresponding to the selected device target according to the application content, and store the single request supervision data in the request type data table associated with the corresponding selected device target according to the obtained request type; The overall supervision module of commodity supply chain information processing targets is used to screen the validity of all single request supervision data corresponding to different equipment targets performing information processing in the commodity supply chain, obtain the request monitoring and screening data sets corresponding to different equipment targets, perform local operation status analysis and overall operation status analysis of the equipment targets in different dimensions based on the request monitoring and screening data sets, and dynamically optimize the information processing corresponding to different equipment targets based on the analysis results; Among them, when the validity screening is performed on all the single request supervision data corresponding to different equipment targets, the application processing degrees in all the single request supervision data of the equipment targets are respectively input into the request supervision identification piecewise function for data analysis and the corresponding request processing identifier is output; Among them, the expression of the request supervision identification piecewise function is ; In the formula, SC0 is the application processing standard value of the device target corresponding to the application instruction; The request processing identifier contains a value of 0 or 1. A request processing identifier with a value of 0 indicates that the corresponding single request processing status is normal, and a request processing identifier with a value of 1 indicates that the corresponding single request processing status is abnormal; According to the request processing identifier with a value of 1, the single request supervision data is marked as valid screening data, and all valid screening data corresponding to the device target are sorted and combined to obtain the request monitoring screening data set corresponding to the device target; When performing self-request local operation status processing analysis and collaborative request local operation status processing analysis on the valid screening data corresponding to all self-request types and all collaborative request types in the request monitoring screening data set, respectively, the application processing degree in the valid screening data corresponding to all self-request types is obtained and marked as SCi, i={1, 2, 3, ..., n}, n is a positive integer; i is the valid screening data corresponding to all self-request types; and the application processing degree in the valid screening data corresponding to all collaborative request types is obtained and marked as SCj, j={1, 2, 3, ..., m}, m is a positive integer; j is the valid screening data corresponding to all collaborative request types; Processing formula through the first running state Calculate and obtain the first local operation status YZy corresponding to all self-request types; where SCi0 is the application processing standard value corresponding to the effective screening data of different self-request types; A is the self-request integration standard value; N1 is the total number of application requests of all self-request types processed by the device target; N is the total number of all application requests processed by the device target; And, through the second operating state processing formula Calculate and obtain the second local operation status EZy corresponding to all collaborative request types; where SCj0 is the application processing standard value corresponding to different effective screening data of collaborative request types; B is the collaborative request integration standard value; N2 is the total number of application requests for all collaborative request types processed by the device target; The first local operating state degree and the second local operating state degree obtained by calculating the equipment target are analyzed by integrating the operating state identification piecewise function to obtain the overall operating state identifier corresponding to the equipment target; Among them, the expression of the integrated running state identification piecewise function is: ; In the formula, ZY is the overall operating status identification; α, β are different weight coefficients, and α+β=1; C is the overall operating status standard value; The overall operation status flag includes a value of 0 or 1. The overall operation status flag with a value of 0 indicates that the information processing status corresponding to the device target is normal, and the overall operation status flag with a value of 1 indicates that the information processing status corresponding to the device target is abnormal; Targeted information processing optimization management is implemented for different equipment targets based on the overall operating status identification obtained through processing.

2. The commodity supply chain visualization supervision system according to claim 1 is characterized in that: The device target corresponding to the overall operation status identifier with a value of 1 is marked as a selected device target, and targeted information processing optimization management is implemented for all selected device targets.

3. A method for visually supervising a commodity supply chain, using a visually supervising commodity supply chain system as described in any one of claims 1 to 2, characterized in that: include: Obtain all equipment targets for information processing in the commodity supply chain and implement different aspects of operation data monitoring and statistics, and digitally process the operation data monitored and counted in different aspects to obtain the operation supervision processing sequence corresponding to the equipment targets; All single request supervision data corresponding to different equipment targets performing information processing in the commodity supply chain are screened for validity, and request monitoring and screening data sets corresponding to different equipment targets are obtained. Based on the request monitoring and screening data sets, local operation status analysis and overall operation status analysis are performed on the equipment targets in different dimensions, and information processing corresponding to different equipment targets is dynamically optimized and managed based on the analysis results.

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