Traditional Chinese medicinal material full-link monitoring system based on artificial intelligence

Abstract

The invention relates to the technical field of link monitoring, in particular to a traditional Chinese medicinal material full-link monitoring system based on artificial intelligence, and the system comprises the steps: extracting a traditional Chinese medicinal material information set stored in a link monitoring platform; on the basis of a traditional Chinese medicinal material information set stored in the link monitoring platform, revising dimensions from an information unit, and analyzing each link of traditional Chinese medicinal materials to obtain information confidence; comparing the information confidence with a preset information confidence, and evaluating the traceability of each link of the traditional Chinese medicinal materials; according to the frequency of each information unit in the monitoring period, the calibration period of the information unit is determined, meanwhile, according to the revision amplitude of the information unit, the specific gravity coefficient of the information unit is determined, the calibration period and the specific gravity coefficient of the information unit are processed, and the monitoring period of each information unit is obtained; according to the invention, it is effectively ensured that the data information involved in the whole link of the traditional Chinese medicinal materials is more accurate, and the frequency of tampering the data information involved in the whole link of the traditional Chinese medicinal materials is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of link monitoring, and in particular to a full-link monitoring system for Chinese medicinal materials based on artificial intelligence. Background Art

[0002] Consumers are paying more and more attention to the safety of Chinese herbal medicines, and have put forward higher requirements for the quality, safety, and effectiveness of Chinese herbal medicines. Now, by giving each piece of Chinese herbal medicine a unique traceability code, the entire process of production, circulation, and consumption of Chinese herbal medicines can be traced; In the existing technology, malicious access may occur in each link of the entire chain of Chinese medicinal materials, and the information of Chinese medicinal materials may be tampered with, resulting in the inability to quickly and accurately verify the data of Chinese medicinal materials. Summary of the invention

[0003] The purpose of the present invention is to provide a full-link monitoring system for Chinese medicinal materials based on artificial intelligence to solve at least one of the above-mentioned prior art problems.

[0004] An artificial intelligence-based full-link monitoring system for Chinese medicinal materials, including: Information collection module: obtain the monitoring platform involved in each link of Chinese medicinal materials, and extract the Chinese medicinal materials information set stored in the link monitoring platform; Information analysis module: Based on the information set of Chinese medicinal materials stored in the link monitoring platform, each link of Chinese medicinal materials is analyzed from the dimension of information unit revision to obtain information confidence; the information unit is the specific information included in the information set of the production monitoring platform, circulation monitoring platform, and sales monitoring platform of Chinese medicinal materials; Information evaluation module: Based on the information confidence of each link of Chinese medicinal materials, the information confidence is compared with the preset information confidence to evaluate the traceability of each link of Chinese medicinal materials; Monitoring and control module: Based on the signal that is not easy to trace, the calibration period of each information unit is determined according to the frequency of each information unit in the monitoring period. At the same time, the revision range of the information unit is obtained by comparing the information unit before revision with the information unit after revision. According to the revision range of the information unit, the weight coefficient of the information unit is determined, and the calibration period of the information unit and the weight coefficient are processed to obtain the monitoring period of each information unit.

[0005] As a further technical solution of the present invention: the link monitoring platform includes a production monitoring platform of Chinese medicinal materials, a circulation monitoring platform of Chinese medicinal materials, and a sales monitoring platform of Chinese medicinal materials.

[0006] As a further technical solution of the present invention: in the information analysis module, during the monitoring period, for each link monitoring platform, relevant parameters for the revision of each information unit are obtained, and the relevant parameters include the number of information unit revisions and the time of the information unit revisions; based on the relevant parameters of each revised information unit, the weight coefficient of each information unit is obtained.

[0007] As a further technical solution of the present invention: the process of obtaining the information confidence of each link is: And, obtaining the revision frequency of each information unit in the monitoring period, multiplying the revision frequency of each information unit in the monitoring period by the weight coefficient to obtain the influence range of each information unit; The influence ranges of all information units in each link monitoring platform are averaged to obtain the information confidence of each link.

[0008] As a further technical solution of the present invention: the specific process of obtaining the weight coefficient of each information unit is: The weight coefficient of each information unit is calculated by the number of information unit revisions and the time of information unit revisions. The calculation formula is as follows: Wherein, X represents the weight coefficient of each information unit, i=1,2,...,n, n represents the number of information unit revisions involved, N represents the number of all information units in the link monitoring platform, Ti represents the time of revision of the i-th information unit, and Tmax represents the maximum time of information unit revision.

[0009] As a further technical solution of the present invention: in the information evaluation module, if the information confidence is greater than or equal to the preset information confidence, a signal that is not convenient for tracing is generated.

[0010] As a further technical solution of the present invention: the process of obtaining the calibration cycle is: Obtain the total number of revisions of the information unit during the monitoring period, and the time node corresponding to each revision; calculate the time interval between two adjacent revisions based on the time node corresponding to each revision, extract the minimum time interval between two adjacent revisions, and record it as the calibration period of the information unit.

[0011] As a further technical solution of the present invention: the process of obtaining the revision range is: The number of characters of the information unit before revision is obtained, the revised information unit is compared with the information unit before revision, the number of modified characters of the information unit is extracted, and the number of modified characters of the information unit is ratio-processed with the number of characters of the information unit before revision to obtain the revision range of the information unit.

[0012] As a further technical solution of the present invention: the process of obtaining the weight coefficient of the information unit is: Wherein, K is the weight coefficient of the information unit, j=1,2,...,m, m represents the total number of revisions of the information unit during the monitoring period, and Dj represents the revision extent of the information unit at the jth revision.

[0013] As a further technical solution of the present invention: the specific process of processing the calibration period and the specific gravity coefficient of the information unit is: The calibration period of the information unit is multiplied by the weight coefficient to obtain the monitoring period of each information unit.

[0014] Beneficial effects of the present invention: The present invention obtains the monitoring platform involved in each link of Chinese medicinal materials, extracts the Chinese medicinal materials information set stored in the link monitoring platform; based on the Chinese medicinal materials information set stored in the link monitoring platform, each link of the Chinese medicinal materials is analyzed from the information unit revision dimension to obtain information confidence; based on the information confidence of each link of the Chinese medicinal materials, the information confidence is compared with the preset information confidence to evaluate the traceability of each link of the Chinese medicinal materials; the technical solution of the embodiment of the present invention: the information data in the monitoring platform connected to any link in the whole link of the Chinese medicinal materials is analyzed from the revision mode, and the impact caused by the modification is digitally evaluated, so that the information data involved in each link can be traced and evaluated, and the revision degree of the information data involved in each link can also be evaluated, that is, when the information data involved in the link is revised and modified, whether it is convenient to trace and search later.

[0015] The present invention is based on the fact that it is inconvenient to trace signals. According to the frequency of each information unit in the monitoring period, the calibration period of the information unit is determined. At the same time, the revision range of the information unit is obtained by comparing the information unit before revision with the information unit after revision. According to the revision range of the information unit, the weight coefficient of the information unit is determined. The calibration period and the weight coefficient of the information unit are processed to obtain the monitoring period of each information unit. The present invention identifies the monitoring period of each information unit based on the data analyzed in the monitoring period, so that the supervisor can timely approve each information unit according to the analyzed period, which can effectively ensure that the data information involved in the whole chain of Chinese medicinal materials is more accurate, and effectively avoid the frequency of tampering with the data information involved in the whole chain of Chinese medicinal materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a system block diagram of a full-link monitoring system for Chinese medicinal materials based on artificial intelligence according to Embodiment 1 of the present invention; Figure 2 This is a system block diagram of a full-link monitoring system for Chinese medicinal materials based on artificial intelligence provided by Embodiment 2 of the present invention; Figure 3 It is a flowchart of a method for full-link monitoring of Chinese medicinal materials based on artificial intelligence provided in Embodiment 3 of the present invention; Figure 4 It is a structural diagram of the device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 should fall within the scope of protection of the present invention.

[0019] Embodiment 1 Figure 1 This is a structural block diagram of a full-link monitoring system for Chinese medicinal materials based on artificial intelligence provided by the first embodiment of the present invention. The embodiment of the present invention can be applied to situations where malicious access occurs in each link of the full-link of Chinese medicinal materials, and the information of Chinese medicinal materials is tampered with, resulting in the inability to quickly and accurately verify the data of Chinese medicinal materials; The relevant explanation about the full chain of Chinese medicinal materials is: the full chain of Chinese medicinal materials includes: the production, circulation, and sales chain of Chinese medicinal materials, etc.

[0020] like Figure 1 As shown, an embodiment of the present invention provides an artificial intelligence-based Chinese medicinal material full-link monitoring system, which specifically includes the following modules: Information collection module: obtain the monitoring platform involved in each link of Chinese medicinal materials, and extract the Chinese medicinal materials information set stored in the link monitoring platform; Among them, the link monitoring platform includes the production monitoring platform of Chinese medicinal materials, the circulation monitoring platform of Chinese medicinal materials, and the sales monitoring platform of Chinese medicinal materials; The information set of the production monitoring platform of Chinese herbal medicines includes: production batch number, production date, manufacturer, production equipment, production personnel, source of raw materials and other information of Chinese herbal medicines; The information set of the circulation monitoring platform of Chinese medicinal materials includes: the transportation date, transportation method, transportation company, storage location, storage temperature, storage time and other information of Chinese medicinal materials; The information set of the sales monitoring platform of Chinese medicinal materials includes: sales date, sales channel, sales quantity, sales price, sales area and other information of Chinese medicinal materials.

[0021] Information analysis module: Based on the information set of Chinese medicinal materials stored in the link monitoring platform, each link of Chinese medicinal materials is analyzed from the dimension of information unit revision to obtain information confidence; the information unit is the specific information included in the information set of the production monitoring platform, circulation monitoring platform, and sales monitoring platform of Chinese medicinal materials, such as the production batch number, production date, transportation date, sales date, etc. of Chinese medicinal materials; Among them, from the dimension of information unit revision, the specific process of analyzing each Chinese medicinal material information set is as follows: Setting a monitoring period, the length of which can be preset by a person skilled in the art, preferably, the monitoring period can be set to 1 hour; During the monitoring cycle, for each link monitoring platform, obtain the relevant parameters of each information unit revision, including the number of information unit revisions and the time of information unit revisions; wherein, information unit revision means modifying the original data of the information unit to data different from the previous data, for example, modifying the original production batch number of October 12, 2023 at 8:52:18 to October 12, 2023 at 8:59:18; According to the relevant parameters of each revised information unit, a weight coefficient of each information unit is obtained; And, obtaining the revision frequency of each information unit in the monitoring period, multiplying the revision frequency of each information unit in the monitoring period by the weight coefficient to obtain the influence range of each information unit; The influence range of all information units in each link monitoring platform is averaged to obtain the information confidence of each link; First, specifically, the process of obtaining the weight coefficient of each information unit is as follows: In a certain link monitoring platform, when any information unit in the information set is initially changed, the number of other information units that have also changed is counted, which is recorded as the number of information unit revisions involved, and the time from the initial change of other information units to the information unit is counted, which is recorded as the information unit revision time; The weight coefficient of each information unit is calculated by the number of information unit revisions and the time of information unit revisions. The calculation formula is as follows: Where X represents the weight coefficient of each information unit, i=1,2,...,n, n represents the number of information unit revisions involved, N represents the number of all information units in the link monitoring platform, T i Indicates the time when the i-th information unit is revised, T max The maximum value of the time when the information unit is revised; Second, specifically, the process of obtaining the frequency of each information unit in the monitoring period is as follows: Count the total number of revisions of the information unit within the monitoring period, and perform ratio processing on the total number of revisions and the monitoring period to obtain the frequency of each information unit in the monitoring period; Exemplarily, during the monitoring period, for the production monitoring platform of Chinese medicinal materials, the number of information units involved in the revision of the production batch number of the Chinese medicinal materials is obtained, which is recorded as a value A, and the time of the revision of the information unit is recorded as a value B; and the revision frequency of the production batch number of the Chinese medicinal materials during the monitoring period is recorded as a value C, wherein the values ​​of A, B, and C are subject to actual statistics; The weight coefficient of the production batch number of Chinese herbal medicines during revision is calculated by the following formula; ; Where X represents the weight coefficient of the production batch number of Chinese herbal medicine when it is revised, i=1,2,...,A, A represents the number of information units involved in the revision, N represents the number of all information units in the link monitoring platform, B A Indicates the time when the Ath information unit was revised, B max The maximum value of the time when the information unit is revised; The revision frequency C of the production batch number of the Chinese herbal medicine in the monitoring period is multiplied by the weight coefficient X to obtain the impact range of each information unit; Information evaluation module: Based on the information confidence of each link of Chinese medicinal materials, the information confidence is compared with the preset information confidence to evaluate the traceability of each link of Chinese medicinal materials; The process of comparing the information confidence with the preset information confidence is as follows: If the information confidence level is greater than or equal to the preset information confidence level, a signal indicating that it is not convenient for tracing is generated; If the information confidence level is less than the preset information confidence level, a signal is generated to facilitate tracing; It needs to be explained that the meaning of the signal that is not convenient for tracing is that when a specific information unit in a certain link analyzed by the Chinese medicinal materials is tampered with in the monitoring platform involved in the link, it will cause a series of modifications to multiple other information units, which has a large triggering and associated impact range. In addition, there is a high frequency of tampering with information units, which will cause the information data contained in the monitoring platform of the link to be tampered with, revised, and modified. In the subsequent verification process, it is not convenient to verify and trace, so as to ensure the accuracy of the Chinese medicinal materials link data; The meaning of the traceability signal is: in one of the links analyzed by the Chinese medicinal materials, when a specific information unit is tampered with in the monitoring platform involved in the link, it will not cause a series of modifications to multiple other information units, and there will be a small triggering and associated impact range. In addition, the frequency of tampering with the information unit is low, which will make it easier to check and trace the information data contained in the monitoring platform of the link when it is tampered, revised, or modified. In the subsequent verification process, it is easy to check and trace, so as to ensure the accuracy of the Chinese medicinal materials link data; The technical solution of the embodiment of the present invention is as follows: the monitoring platform involved in each link of Chinese medicinal materials is obtained, and the Chinese medicinal materials information set stored in the link monitoring platform is extracted; based on the Chinese medicinal materials information set stored in the link monitoring platform, each link of the Chinese medicinal materials is analyzed from the information unit revision dimension to obtain information confidence; based on the information confidence of each link of the Chinese medicinal materials, the information confidence is compared with the preset information confidence to evaluate the traceability of each link of the Chinese medicinal materials; the technical solution of the embodiment of the present invention is as follows: the information data in the monitoring platform connected to any link in the entire link of Chinese medicinal materials is analyzed from the revision mode, and the impact caused by the modification is digitally evaluated, so that the information data involved in each link can be traced and evaluated, and the revision degree of the information data involved in each link can also be evaluated, that is, when the information data involved in the link is revised and modified, whether it is convenient to trace and search later.

[0022] Embodiment 2 Figure 2 A structural block diagram of a full-link monitoring system for Chinese medicinal materials based on artificial intelligence provided in Embodiment 2 of the present invention is shown in FIG. Figure 2 As shown, an embodiment of the present invention provides an artificial intelligence-based full-link monitoring system for Chinese medicinal materials, which specifically also includes the following modules: Monitoring and control module: Based on the signal that is not easy to trace, the link corresponding to the signal that is not easy to trace is analyzed, and the calibration period of each information unit is determined according to the frequency of each information unit in the monitoring period. At the same time, the revision range of the information unit is obtained by comparing the information unit before revision with the information unit after revision. According to the revision range of the information unit, the weight coefficient of the information unit is determined, and the calibration period of the information unit and the weight coefficient are processed to obtain the monitoring period of each information unit; First, specifically, according to the frequency of each information unit in the monitoring period, the specific process of determining the calibration period of the information unit is: Obtain the total number of revisions of the information unit during the monitoring period, and the time node corresponding to each revision; calculate the time interval between two adjacent revisions according to the time node corresponding to each revision, extract the minimum time interval between two adjacent revisions, and record it as the calibration period of the information unit; Second, specifically, by comparing the information unit before revision with the information unit after revision, the specific process of obtaining the revision range of the information unit is as follows: Obtaining the number of characters of the information unit before revision, comparing the revised information unit with the information unit before revision, extracting the number of characters modified in the information unit, performing ratio processing on the number of characters modified in the information unit and the number of characters in the information unit before revision, and obtaining the revision range of the information unit; Thirdly, specifically, according to the revision range of the information unit, the specific process of determining the weight coefficient of the information unit is as follows: Obtain the total number of revisions of the information unit during the monitoring period, as well as the revision range analyzed each time the information unit is revised, and calculate the weight coefficient of the information unit through the formula, which is as follows: Where K is the weight coefficient of the information unit, j = 1, 2, ..., m, m represents the total number of revisions of the information unit during the monitoring period, D j Indicates the revision extent of the information unit at the jth revision; Fourthly, the specific process of processing the calibration period and the weight coefficient of the information unit is as follows: The calibration period of the information unit is multiplied by the weight coefficient to obtain the monitoring period of each information unit; The technical solution of the embodiment of the present invention: based on the inconvenience of tracing signals, the calibration period of each information unit is determined according to the frequency of each information unit in the monitoring period, and at the same time, the revision range of the information unit is obtained by comparing the information unit before revision with the information unit after revision, and the weight coefficient of the information unit is determined according to the revision range of the information unit, and the calibration period and weight coefficient of the information unit are processed to obtain the monitoring period of each information unit; the present invention identifies the monitoring period of each information unit based on the data analyzed in the monitoring period, so that the supervisor can promptly approve each information unit according to the analyzed period, which can effectively ensure that the data information involved in the whole chain of Chinese medicinal materials is more accurate, and effectively avoid the frequency of tampering with the data information involved in the whole chain of Chinese medicinal materials.

[0023] Embodiment 3 Figure 3 A structural block diagram of a full-link monitoring method for Chinese medicinal materials based on artificial intelligence provided in Embodiment 2 of the present invention, such as Figure 2 As shown, an embodiment of the present invention provides a full-link monitoring method for Chinese medicinal materials based on artificial intelligence, which specifically includes the following steps: Step 1: Obtain the monitoring platform involved in each link of Chinese medicinal materials, and extract the Chinese medicinal materials information set stored in the link monitoring platform; Step 2: Based on the Chinese medicinal materials information set stored in the link monitoring platform, analyze each link of the Chinese medicinal materials from the information unit revision dimension to obtain information confidence; the information unit is the specific information included in the information set of the production monitoring platform, circulation monitoring platform, and sales monitoring platform of the Chinese medicinal materials, such as the production batch number, production date, transportation date, sales date, etc. of the Chinese medicinal materials; Step 3: Based on the information confidence of each link of the Chinese medicinal materials, the information confidence is compared with the preset information confidence to evaluate the traceability of each link of the Chinese medicinal materials; Step 4. Based on the signal that is not easy to trace, determine the calibration period of each information unit according to the frequency of each information unit in the monitoring period. At the same time, compare the information unit before revision with the information unit after revision to obtain the revision range of the information unit. According to the revision range of the information unit, determine the weight coefficient of the information unit. Process the calibration period of the information unit and the weight coefficient to obtain the monitoring period of each information unit.

[0024] Embodiment 4 Reference Figure 4The embodiment of the present invention further provides a computer device, including: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a backlight module processing coating flatness monitoring method based on machine vision as described in any one of the above methods is implemented.

[0025] The computer device may be a desktop computer, a notebook, a PDA, a cloud server or other computing device. The computer device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0026] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0027] In some embodiments, the memory 302 may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 302 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory 302 may also include both an internal storage unit of the computer device and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0028] Embodiment 5 An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for monitoring the flatness of a backlight module coating based on machine vision as described in any one of the above methods is implemented.

[0029] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0030] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0031] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0032] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0033] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0034] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A full-link monitoring system for Chinese medicinal materials based on artificial intelligence, characterized in that: include: Information collection module: obtain the monitoring platform involved in each link of Chinese medicinal materials, and extract the Chinese medicinal materials information set stored in the link monitoring platform; Information analysis module: Based on the Chinese medicinal materials information set stored in the link monitoring platform, each link of the Chinese medicinal materials is analyzed from the information unit revision dimension to obtain the information confidence; The information unit is the specific information included in the information set of the production monitoring platform, circulation monitoring platform, and sales monitoring platform of Chinese medicinal materials; Information evaluation module: Based on the information confidence of each link of Chinese medicinal materials, the information confidence is compared with the preset information confidence to evaluate the traceability of each link of Chinese medicinal materials; Monitoring and control module: Based on the signal that is not easy to trace, the calibration period of each information unit is determined according to the frequency of each information unit in the monitoring period. At the same time, the revision range of the information unit is obtained by comparing the information unit before revision with the information unit after revision. According to the revision range of the information unit, the weight coefficient of the information unit is determined, and the calibration period of the information unit and the weight coefficient are processed to obtain the monitoring period of each information unit.

2. According to claim 1, a full-link monitoring system for Chinese medicinal materials based on artificial intelligence is characterized in that: The link monitoring platform includes the production monitoring platform of Chinese medicinal materials, the circulation monitoring platform of Chinese medicinal materials, and the sales monitoring platform of Chinese medicinal materials.

3. According to claim 1, a blockchain-based password security management system is characterized in that: In the information analysis module, during the monitoring period, for each link monitoring platform, the relevant parameters of each information unit revision are obtained, and the relevant parameters include the number of information unit revisions and the time of information unit revisions. According to the relevant parameters of each revised information unit, the weight coefficient of each information unit is obtained.

4. A blockchain-based password security management system according to claim 3, characterized in that: The process of obtaining the information confidence of each link is as follows: And, obtaining the revision frequency of each information unit in the monitoring period, multiplying the revision frequency of each information unit in the monitoring period by the weight coefficient to obtain the influence range of each information unit; The influence ranges of all information units in each link monitoring platform are averaged to obtain the information confidence of each link.

5. According to claim 4, a full-link monitoring system for Chinese medicinal materials based on artificial intelligence is characterized in that: The specific process of obtaining the weight coefficient of each information unit is as follows: The weight coefficient of each information unit is calculated by the number of information unit revisions and the time of information unit revisions. The calculation formula is as follows: Where X represents the weight coefficient of each information unit, i=1,2,...,n, n represents the number of information unit revisions involved, N represents the number of all information units in the link monitoring platform, T i Indicates the time when the i-th information unit is revised, T max Indicates the maximum value of the time when the information unit was revised.

6. According to claim 1, a full-link monitoring system for Chinese medicinal materials based on artificial intelligence is characterized in that: In the information evaluation module, if the information confidence is greater than or equal to the preset information confidence, a signal that is not convenient for tracing is generated.

7. According to claim 6, a full-link monitoring system for Chinese medicinal materials based on artificial intelligence is characterized in that: The process of obtaining the calibration cycle is: Obtain the total number of revisions of the information unit during the monitoring period, and the time node corresponding to each revision; calculate the time interval between two adjacent revisions based on the time node corresponding to each revision, extract the minimum time interval between two adjacent revisions, and record it as the calibration period of the information unit.

8. According to claim 7, a full-link monitoring system for Chinese medicinal materials based on artificial intelligence is characterized in that: The process of obtaining the revision range is as follows: The number of characters of the information unit before revision is obtained, the revised information unit is compared with the information unit before revision, the number of modified characters of the information unit is extracted, and the number of modified characters of the information unit is ratio-processed with the number of characters of the information unit before revision to obtain the revision range of the information unit.

9. The artificial intelligence-based Chinese medicinal material full-link monitoring system according to claim 8 is characterized in that: The process of obtaining the weight coefficient of the information unit is as follows: Where K is the weight coefficient of the information unit, j = 1, 2, ..., m, m represents the total number of revisions of the information unit during the monitoring period, D j Indicates the revision extent of the information unit at the jth revision.

10. The artificial intelligence-based Chinese medicinal material full-link monitoring system according to claim 9, characterized in that: The specific process of processing the calibration period and the specific gravity coefficient of the information unit is as follows: The calibration period of the information unit is multiplied by the weight coefficient to obtain the monitoring period of each information unit.

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