CN114282745A - Risk early warning method for wading product production enterprise and related equipment - Google Patents
Risk early warning method for wading product production enterprise and related equipment Download PDFInfo
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Abstract
The method comprises the steps that a terminal of a production enterprise acquires production data of the wading product production enterprise, a server of a supervision department acquires production operation data and enterprise evaluation data of the wading product production enterprise, the production data, the production operation data and the enterprise evaluation data are randomly disturbed and mixed to obtain a data set to be evaluated, and the data set to be evaluated is sent to relative weight terminals with preset quantity; each relative weight terminal gives relative weight to any two data to be evaluated in the data set to be evaluated to obtain a relative weight number set, the supervision department server determines the weight of the data to be evaluated based on the relative weight number set, generates a risk level of the wading product production enterprise based on the data to be evaluated and the weight, and sends early warning information to the production enterprise terminal based on the risk level, so that the accuracy of determining the potential risk of the wading product production enterprise is improved.
Description
Technical Field
The disclosure relates to the technical field of enterprise risk early warning, in particular to a risk early warning method and related equipment for an enterprise producing water-related products.
Background
The wading product refers to a product relating to the sanitary and safe drinking water, and all the connecting water-stopping materials, plastics and organic synthetic pipes, pipe fittings, protective coatings, water treatment agents, scale removers, water quality processors and other materials and chemical substances which are contacted with the drinking water in the production and water supply processes of the drinking water belong to the wading products. The wading product is closely related to the life and the body health of the masses and directly related to each citizen. The good ring of the wading product is directly determined by the manufacturing enterprise of the wading product, so that the risk early warning of the manufacturing enterprise of the wading product is very important.
At present, risk early warning of wading product production enterprises is mainly achieved by collecting data through a network and analyzing and evaluating the data by appointed experts, and then the risk level of each wading product production enterprise is obtained.
Disclosure of Invention
In view of this, the present disclosure provides a risk early warning method for an aquatic product manufacturing enterprise and a related device.
Based on the above purpose, the present disclosure provides a risk early warning method for an wading product production enterprise, wherein the method is applied to a risk management and control system for the wading product production enterprise, the system includes a production enterprise terminal, a supervision department server and a relative weight terminal, and the method includes:
the manufacturing enterprise terminal acquires the production data of the wading product manufacturing enterprise and sends the production data to the supervision department server;
the supervision department server acquires production operation data and enterprise evaluation data of the wading product production enterprise through a web crawler;
the supervision department server randomly scrambles and mixes the production data, the production operation data and the enterprise evaluation data to obtain a data set to be evaluated, and sends the data set to be evaluated to a preset number of relative weight terminals;
each relative weight terminal endows any two data to be evaluated in the data set to be evaluated with relative weight to obtain a relative weight set, and sends the relative weight set to the supervision department server;
and the supervision department server determines the weight of the data to be evaluated based on the relative weight number set sent by all the relative weight terminals, generates a risk level of the water-involved product production enterprise based on the data to be evaluated and the weight, and sends early warning information to the production enterprise terminal based on the risk level.
Correspondingly, the present disclosure also provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable by the processor, and when the processor executes the program, the risk early warning method for the wading product manufacturing enterprise is implemented.
Accordingly, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the risk pre-warning method for an wading product manufacturing enterprise according to any of the above embodiments.
As can be seen from the above, according to the risk early warning method for the wading product manufacturing enterprise provided by the disclosure, the manufacturing enterprise terminal acquires the production data of the wading product manufacturing enterprise and sends the production data to the supervision department server; the supervision department server acquires production operation data and enterprise evaluation data of the wading product production enterprise through a web crawler; the supervision department server randomly scrambles and mixes the production data, the production operation data and the enterprise evaluation data to obtain a data set to be evaluated, and sends the data set to be evaluated to a preset number of relative weight terminals; each relative weight terminal endows any two data to be evaluated in the data set to be evaluated with relative weight to obtain a relative weight set, and sends the relative weight set to the supervision department server; the monitoring department server determines the weight of the data to be evaluated based on the relative weight number set sent by all the relative weight terminals, generates the risk level of the wading product production enterprise based on the data to be evaluated and the weight, and sends early warning information to the production enterprise terminal based on the risk level, so that the accuracy of determining the potential risk of the wading product production enterprise is improved, the enterprise is informed to correct in time, and the adverse effect of the wading product on the body health of people is avoided.
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In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings needed to be used in the description of the embodiments or related technologies are briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a risk early warning method for an aquatic product manufacturing enterprise according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a risk early warning system of an aquatic product manufacturing enterprise according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a specific electronic device according to an embodiment of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that technical terms or scientific terms used in the embodiments of the present disclosure should have a general meaning as understood by those having ordinary skill in the art to which the present disclosure belongs, unless otherwise defined. The use of "first," "second," and similar terms in the embodiments of the disclosure is not intended to indicate any order, quantity, or importance, but rather to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
As described in the background art, the existing risk early warning of the wading product production enterprise mainly collects data through a network and is analyzed and evaluated by a designated expert, firstly, the data of the wading product production enterprise is acquired through the network and is relatively single, and generally only the business information and the social evaluation information of some enterprises are included, so that not only the reliability cannot be ensured, but also the production data of the enterprises cannot be well mastered, and thus, the evaluation result is relatively comprehensive when the risk evaluation is performed. Meanwhile, different weights are directly given to each data to be evaluated by an appointed expert according to the acquired network data, and then the weights are used for carrying out risk evaluation on the enterprise, so that the enterprise is easily influenced by subjective factors of the expert. Therefore, the method includes the steps that production data of a wading product production enterprise are obtained through a production enterprise terminal directly arranged in the local of the wading product production enterprise, then a monitoring department server obtains production operation data and enterprise evaluation data of the wading product production enterprise through a web crawler, the three data are mixed in a disorderly mode and are used as a data set to be evaluated together, after the data set to be evaluated is obtained, the data set to be evaluated is sent to a plurality of relative weight terminals, each relative weight terminal gives relative weight to any two data to be evaluated in the data set to be evaluated to obtain a relative weight number set, the weight of the data to be evaluated is determined through the relative weight number sets sent by all the relative weight terminals, and based on the data to be evaluated and the weight, the risk level of the wading product production enterprise is generated, and finally, early warning information is sent to the production enterprise terminal according to the risk level, so that when data are acquired, enterprise data can be collected from two ways of a network end and an enterprise production field, the comprehensiveness of the data is ensured, the data are mixed in a disorderly mode, the influence of human subjective factors on data sources is avoided, the assignment of the relative weights of any two data to be evaluated is carried out through a plurality of relative weight terminals respectively, the priority relation of the importance of each data to be evaluated is reflected, the speaking weight of a single expert on the whole evaluation result is weakened, and the accuracy of enterprise risk estimation is further improved.
Referring to fig. 1, which is a schematic flow chart of a risk early warning method for an wading product manufacturing enterprise according to an embodiment of the present disclosure, the method is applied to a risk management and control system for the wading product manufacturing enterprise, referring to fig. 2, the system includes a manufacturing enterprise terminal 201, a supervision department server 202, and a relative weight terminal 203, and the method includes the following steps:
and S101, the terminal of the production enterprise acquires production data of the wading product production enterprise and sends the production data to the server of the supervision department.
During specific implementation, the terminal of the production enterprise is arranged locally in the production enterprise of the wading product, optionally, the terminal of the production enterprise comprises various sensors arranged on a production line and is used for acquiring production data of the enterprise, the production data comprises yield and quality of the wading product and other detection index data, it needs to be stated that in order to ensure accuracy of the acquired production data, the terminal of the production enterprise is arranged locally in the enterprise but is set to operate in an automatic closed mode, and the production enterprise of the wading product cannot operate the terminal of the production enterprise and can only passively receive early warning information sent by the terminal of the production enterprise.
And S102, the supervision department server acquires the production operation data and the enterprise evaluation data of the wading product production enterprise through a web crawler.
In specific implementation, the server of the supervision department acquires the production operation data and the enterprise evaluation data of the wading product production enterprise through the web crawler. The production and management data mainly comprises industry and commerce information data, tax information data and public financial information data of enterprises, and the production and management data is mainly acquired through official public data. The enterprise evaluation data mainly comprises legal complaint information data of enterprises, user complaint information data and evaluation information data of all social circles.
S103, the supervision department server randomly scrambles and mixes the production data, the production operation data and the enterprise evaluation data to obtain a data set to be evaluated, and sends the data set to be evaluated to a preset number of relative weight terminals.
In specific implementation, in order to avoid that the relative weight terminal has obvious tendency to judge the importance of the data by acquiring channels, namely, the source of the data is over considered when judging the importance degree of different data, and the importance of the meaning of the data information is ignored. Before data is sent to the relative weight terminal, the data is scrambled and mixed to obtain a data set to be evaluated, the data set to be evaluated comprises a plurality of data to be evaluated, and then the relative weight terminal cannot well judge the data source of each data to be evaluated after receiving the data to be evaluated, and can only judge the data source according to the importance degree of the information of the data. And when the data set to be evaluated is sent, sending the data set to a preset number of the relative weight terminals at the same time. The preset number may be set as needed, and is not limited herein, for example, the preset number may be set to 10.
It should be noted that, in the present disclosure, the number of the relative weight terminals may be multiple, and a specific value is not limited, but the value is generally greater than the preset number, so that it is ensured that the data set to be evaluated sent each time is not received by the same batch of relative weight terminals, but a preset number of relative weight terminals are selected from all the relative weight terminals to receive and process the data to be evaluated.
In some embodiments, sending the data set to be evaluated to a preset number of the relative weight terminals specifically includes:
and the supervision department server sends the data set to be evaluated to a preset number of relative weight terminals based on the selected probability of each relative weight terminal.
In specific implementation, the probability of being selected by each relative weight terminal determines the probability of being sent to the data set to be evaluated by the relative weight terminal, and generally, the greater the probability of being selected by the relative weight terminal, the higher the probability of being sent to the data set to be evaluated by the relative weight terminal. It should be noted that the probability of selection of the relative weight terminal is not completely equal to the priority, and the data set to be evaluated is not necessarily sent with the probability of selection ranked in the front every time the data set to be evaluated is sent, so that the data set to be evaluated is prevented from being received by the same relative weight terminals all the time.
To improve the overall evaluation level of the relative weight terminals, in some embodiments, determining the probability of being selected for each of the relative weight terminals comprises:
and the supervision department server determines the ratio of the effective relative weight to all the relative weights in the historical relative weight number set sent by each relative weight terminal, and determines the selected probability of each relative weight terminal based on the ratio and a preset threshold value.
In specific implementation, the supervision department server determines the selected probability of each relative weight terminal according to the ratio of the number of the effective relative weights in the historical relative weight number set sent by the relative weight terminal to the number of all relative weights and a preset threshold value. The ratio of the effective relative weight to all the relative weights represents the accuracy of the relative weight given by the relative weight terminal, but only the selection probability is determined through the standard, the selection probability of some relative weight terminals can be infinitely increased, the selection probability of each relative weight terminal can be subjected to bipolar differentiation, and when the selection probability is increased to 100%, a data set to be evaluated is inevitably sent to the relative weight terminal corresponding to the selection probability each time. The preset threshold may be set as needed, for example, the preset threshold may be set to 30%.
And S104, each relative weight terminal endows relative weight to any two data to be evaluated in the data sets to be evaluated to obtain a relative weight number set, and sends the relative weight number set to the supervision department server.
In specific implementation, after receiving the data set to be evaluated, each relative weight terminal assigns a relative weight to any two data to be evaluated in the data set to be evaluated, so as to obtain a relative weight set composed of all relative weights, where the relative weight represents a ratio of a weight of one data to be evaluated to a weight of the other data to be evaluated in the two data to be evaluated, for example, the weight of the first data to be evaluated is 0.1, the weight of the second data to be evaluated is 0.2, and the relative weight of the first data to be evaluated and the second data to be evaluated is 1/2. Since the execution standards of different data to be evaluated are not uniform easily when each data to be evaluated is given a weight independently, and the weight relationship between the data to be evaluated cannot be inverted well, the relative weight terminal gives a relative weight to any two data to be evaluated in the method, rather than directly determining the weight of each data to be evaluated, thereby improving the relevance between the data to be evaluated. And after obtaining the relative weight number set, the relative weight terminal sends the relative weight number set to the supervision department server. Optionally, the relative weight terminal may be a mobile phone, a notebook computer, a third-party server, or other device terminals having a communication function.
It should be noted that, in the present disclosure, the process of assigning a relative weight to any two pieces of to-be-evaluated data in the received to-be-evaluated data set by the relative weight terminal is not limited to the process of receiving the data by a user, and then assigning a relative weight to the to-be-evaluated data through subjective judgment of the user. The process of giving the relative weight may be performed by a neural network model installed at a relative weight terminal, and optionally, the neural network model may be any one of the neural network models disclosed for estimating the weight, or may use the neural network model disclosed as a reference model, then train the reference model through a large amount of wading product data, and then give the relative weight to the data to be estimated by the trained reference model. Optionally, the relative weight terminal may also implement a Process of assigning a relative weight to any two pieces of data to be evaluated in the received data set to be evaluated according to an Analytic Hierarchy Process (AHP).
After receiving the data set to be evaluated, the relative weight terminal may assign a relative weight to any two data to be evaluated in the data set to be evaluated by an expert user, or assign a relative weight to any two data to be evaluated in the data set to be evaluated by artificial intelligence, which is not limited herein.
In some embodiments, the determining, by the regulatory authority server, the weight of the data to be evaluated based on the relative weights sent by all the relative weight terminals specifically includes:
for the relative weight number set sent by each relative weight terminal, the monitoring department server converts the relative weight number set into a judgment matrix and checks whether the judgment matrix is a consistency matrix; in response to determining that the decision matrix is a consistency matrix, determining the relative weights in the decision matrix as valid relative weights; in response to determining that the judgment matrix is not a consistency matrix, subdividing the relative weights in the judgment matrix into a preset number of sub-judgment matrices, and checking whether each sub-judgment matrix is a consistency matrix; in response to determining that the sub-decision matrix is a consistency matrix, determining the relative weights in the sub-decision matrix as the effective relative weights;
and the supervision department server determines the weight of the data to be evaluated based on the effective relative weights corresponding to all the relative weight terminals.
In specific implementation, in order to further improve the accuracy of the relative weight, the monitoring department server converts the relative weight number set sent by each relative weight terminal into a judgment matrix, and screens out the effective relative weight by judging whether the matrix is a consistency matrix. In general, if a certain decision matrix is a consistency matrix, there is no logical conflict between the data in the decision matrix, and all the relative weights in the decision matrix can be regarded as valid relative weights. For example, the relative weight transmitted by the relative weight terminal is the relative weight between three data to be evaluated, and the three data to be evaluated are represented by a, b, and c, wherein in the relative weight transmitted by the relative weight terminal, the relative weight of a and b is 2 (i.e. the weight of a is 2 times that of b), the relative weight of b and c is 2 (i.e. the weight of b is 2 times that of c), and the relative weight of a and c is 3 (i.e. the weight of a is 3 times that of c), but a simple mathematical calculation can be used to obtain that the weight of a should be 4 times that of c, so the relative weight transmitted by the relative weight terminal has a logical error, and the judgment matrix composed of a, b, and c is not a consistency matrix, and therefore none of a, b, and c is considered as an effective relative weight.
Because the number of the relative weights included in the relative weight number set is generally large, if the whole relative weight number set is taken as a whole to judge whether the consistency condition is met, the possibility of meeting the consistency is not high, all the relative weight numbers are probably excluded, and further effective relative weights cannot be obtained, but for a relative weight terminal, when a judged sample is large enough, a part of the data set possibly meets the consistency, the part of the data set can be regarded as effective data, in order to cope with the situation, when the judgment matrix is determined not to be a consistency matrix, the relative weights in the judgment matrix are divided into a preset number of sub-judgment matrices again, and whether each sub-judgment matrix is a consistency matrix is checked; and when the sub judgment matrix is determined to be the consistency matrix, determining the relative weight in the sub judgment matrix as the effective relative weight.
In some embodiments, the determining, by the regulatory agency server, the weight of the data to be evaluated based on the valid relative weights corresponding to all the relative weight terminals includes:
the supervision department server determines a preliminary weight corresponding to each data to be evaluated and each relative weight terminal based on the effective relative weight corresponding to each relative weight terminal;
and the supervision department server determines the average value of the preliminary weights corresponding to each data to be evaluated and all the relative weight terminals as the weight of the data to be evaluated.
In specific implementation, the monitoring department server determines, based on the effective relative weight corresponding to each relative weight terminal, a preliminary weight corresponding to each data to be evaluated and the relative weight terminal, optionally, all effective relative weights may be sorted, then, a relative weight with the smallest sorting is found out from the sorted relative weights, the data to be evaluated with the smallest weight is determined according to the smallest relative weight, a preset preliminary weight is given to the data to be evaluated, and then, the preliminary weights of all data to be evaluated may be obtained. For example, if the relative weight with the smallest rank is the relative weight of the data to be evaluated a and b, and the specific value is 1/2 (that is, the weight of a is 1/2 of the weight of b), it may be determined that the data to be evaluated with the smallest weight is a, and then a preset preliminary weight is given to a, for example, 0.1, then the preliminary weight of b is easily obtained as 0.2, and so on, the preliminary weights of all the data to be evaluated may be obtained. After the preliminary weight corresponding to each piece of data to be evaluated and each relative weight terminal is obtained, the supervision department server determines the average value of the preliminary weights corresponding to each piece of data to be evaluated and all the relative weight terminals as the weight of the data to be evaluated.
In some embodiments, whether the decision matrix or the sub-decision matrix is a consistency matrix is determined by the following formula:
wherein CR represents a matrix consistency index, when CR is larger than a preset threshold value, the detected judgment matrix has consistency, CI represents the matrix consistency index, RI represents the average random consistency index of the matrix,λ max the maximum eigenvalue of the matrix is represented,
represents the average of the maximum eigenvalues of the random matrix,nthe order of the matrix is represented. Optionally, the average value of the maximum eigenvalue of the random matrix can be obtained through experiments, that is, numbers are randomly extracted from 1 to 9 and reciprocal thereof to construct a reciprocal matrix, a sample matrix of a preset value is constructed, then the average value of the maximum eigenvalue of the random matrix of the preset value can be obtained,nthe order of the matrix is represented.
S105, the supervision department server determines the weight of the data to be evaluated based on the relative weight number set sent by all the relative weight terminals, generates the risk level of the water-related product production enterprise based on the data to be evaluated and the weight, and sends early warning information to the production enterprise terminal based on the risk level.
In specific implementation, the monitoring department server firstly determines the weight of the data to be evaluated according to the relative weight number set sent by all the relative weight terminals, then generates the risk level of the wading product production enterprise according to the data to be evaluated and the weight, optionally, firstly performs normalization processing on the data to be evaluated, then multiplies each data to be evaluated by the corresponding weight to obtain the score value of each data to be evaluated, then calculates the sum of the score values of all the data to be evaluated, generates the risk level of the wading product production enterprise according to the position of the sum in a preset risk level comparison table, and after obtaining the risk level of the wading product production enterprise, the monitoring department server sends early warning information to the production enterprise terminal according to the risk level. Optionally, the warning information may include a risk level of the wading product manufacturing enterprise and corrective measures to be taken by the wading product manufacturing enterprise. Optionally, referring to table 1, the preset risk level comparison table is provided, and it should be noted that table 1 is only one implementation scheme provided in this embodiment, and does not represent a limitation to the scheme, and a person skilled in the art may adjust the preset risk level comparison table as needed.
TABLE 1
In some embodiments, before the regulatory agency server randomly shuffles and mixes the production data, the production operation data, and the enterprise valuation data, the method further comprises:
merging the production data, the production operation data and the enterprise evaluation data aiming at the same index into the data to be evaluated based on the respective priority and the occurrence frequency;
the priority of the production data is greater than that of the production operation data, and the priority of the production operation data is greater than that of the enterprise evaluation data.
In specific implementation, the method of the present disclosure obtains three enterprise-related data from different channels, but the three enterprise-related data are likely to have a overlapped part, for example, for an index of the enterprise's one-quarter yield, the three enterprise-related data may exist in production data (which may be obtained by measurement of an on-site sensor), or may exist in production operation data (which may be obtained through a one-quarter financial report published by the enterprise), and at this time, the overlapped index data needs to be merged, thereby reducing subsequent calculation amounts of the monitoring department server and the relative weight terminal. Therefore, before obtaining the data set to be evaluated, the monitoring department server may combine the production data, the production operation data, and the enterprise evaluation data for the same index into one data to be evaluated, where the basis for the combination is mainly the priority and the occurrence frequency of each of the production data, the production operation data, and the enterprise evaluation data. Generally, production data is obtained by direct measurement through sensors arranged on an enterprise production line, so that the priority of the data is highest, and production and management data is mainly obtained through an official platform, so that the priority of the production and management data is higher than that of enterprise evaluation data.
In some embodiments, the production data, the production operation data and the enterprise evaluation data for the same index are combined into one data to be evaluated based on the respective priorities and data occurrence frequencies through the following formulas:
wherein,
representing said data to be evaluated, P1Representing said production data, P2Representing production and management data, P3Representing business valuation data, n1Representing the frequency of occurrence of said production data, I1Representing the priority of said production data, n2Indicating the frequency of occurrence of said production operation data, I2Representing the priority of said production data, n3Indicating the frequency of occurrence of said business valuation data, I3Representing a priority of the enterprise valuation data.
In the above formula, P is1、P2、 P3All can be 0, I1>I2 >I3The specific values of the priorities may be set as desired.
According to the risk early warning method for the wading product production enterprise, the production enterprise terminal obtains production data of the wading product production enterprise and sends the production data to the supervision department server; the supervision department server acquires production operation data and enterprise evaluation data of the wading product production enterprise through a web crawler; the supervision department server randomly scrambles and mixes the production data, the production operation data and the enterprise evaluation data to obtain a data set to be evaluated, and sends the data set to be evaluated to a preset number of relative weight terminals; each relative weight terminal endows any two data to be evaluated in the data set to be evaluated with relative weight to obtain a relative weight set, and sends the relative weight set to the supervision department server; the monitoring department server determines the weight of the data to be evaluated based on the relative weight number set sent by all the relative weight terminals, generates the risk level of the wading product production enterprise based on the data to be evaluated and the weight, and sends early warning information to the production enterprise terminal based on the risk level, so that the accuracy of determining the potential risk of the wading product production enterprise is improved, the enterprise is informed to correct in time, and the adverse effect of the wading product on the body health of people is avoided.
It should be noted that the method of the embodiments of the present disclosure may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the devices may interact with each other to complete the method.
It should be noted that the above describes some embodiments of the disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same inventive concept, corresponding to the method of any embodiment described above, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and operable on the processor, and when the processor executes the program, the risk early warning method for the water-involved product manufacturing enterprise according to any embodiment described above is implemented.
Fig. 3 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device of the above embodiment is used for implementing the risk early warning method of the corresponding wading product manufacturing enterprise in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the risk pre-warning method for an wading product manufacturing enterprise according to any of the above-mentioned embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the risk early warning method for the water-involved product manufacturing enterprise according to any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the present disclosure, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the present disclosure, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present disclosure are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that the embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The disclosed embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalents, improvements, and the like that may be made within the spirit and principles of the embodiments of the disclosure are intended to be included within the scope of the disclosure.
Claims (10)
1. A risk early warning method for a wading product production enterprise is applied to a wading product production enterprise risk management and control system, the system comprises a production enterprise terminal, a supervision department server and a relative weight terminal, and the method comprises the following steps:
the manufacturing enterprise terminal acquires the production data of the wading product manufacturing enterprise and sends the production data to the supervision department server;
the supervision department server acquires production operation data and enterprise evaluation data of the wading product production enterprise through a web crawler;
the supervision department server randomly scrambles and mixes the production data, the production operation data and the enterprise evaluation data to obtain a data set to be evaluated, and sends the data set to be evaluated to a preset number of relative weight terminals;
each relative weight terminal endows any two data to be evaluated in the data set to be evaluated with relative weight to obtain a relative weight set, and sends the relative weight set to the supervision department server;
and the supervision department server determines the weight of the data to be evaluated based on the relative weight number set sent by all the relative weight terminals, generates a risk level of the water-involved product production enterprise based on the data to be evaluated and the weight, and sends early warning information to the production enterprise terminal based on the risk level.
2. The method according to claim 1, wherein the determining, by the regulatory agency server, the weight of the data to be evaluated based on the relative weights sent by all the relative weight terminals specifically includes:
for the relative weight number set sent by each relative weight terminal, the monitoring department server converts the relative weight number set into a judgment matrix and checks whether the judgment matrix is a consistency matrix; in response to determining that the decision matrix is a consistency matrix, determining the relative weights in the decision matrix as valid relative weights; in response to determining that the judgment matrix is not a consistency matrix, subdividing the relative weights in the judgment matrix into a preset number of sub-judgment matrices, and checking whether each sub-judgment matrix is a consistency matrix; in response to determining that the sub-decision matrix is a consistency matrix, determining the relative weights in the sub-decision matrix as the effective relative weights;
and the supervision department server determines the weight of the data to be evaluated based on the effective relative weights corresponding to all the relative weight terminals.
3. The method according to claim 2, wherein the determining, by the regulatory agency server, the weight of the data to be evaluated based on the valid relative weights corresponding to all the relative weight terminals specifically includes:
the supervision department server determines a preliminary weight corresponding to each data to be evaluated and each relative weight terminal based on the effective relative weight corresponding to each relative weight terminal;
and the supervision department server determines the average value of the preliminary weights corresponding to each data to be evaluated and all the relative weight terminals as the weight of the data to be evaluated.
4. The method of claim 2, wherein whether the decision matrix or the sub-decision matrix is a consistency matrix is determined by the following formula:
wherein CR represents a matrix consistency index, when CR is larger than a preset threshold value, the detected judgment matrix has consistency, CI represents the matrix consistency index, RI represents the average random consistency index of the matrix,λ max the maximum eigenvalue of the matrix is represented,
represents the average of the maximum eigenvalues of the random matrix,nthe order of the matrix is represented.
5. The method of claim 1, wherein prior to the regulatory agency server randomly shuffling and blending the production data, the production operation data, and the enterprise valuation data, the method further comprises:
merging the production data, the production operation data and the enterprise evaluation data aiming at the same index into the data to be evaluated based on the respective priority and the occurrence frequency;
the priority of the production data is greater than that of the production operation data, and the priority of the production operation data is greater than that of the enterprise evaluation data.
6. The method of claim 5, wherein the production data, the production operation data and the enterprise evaluation data for the same index are combined into one data to be evaluated based on the respective priorities and data occurrence frequencies by the following formulas:
wherein,
representing said data to be evaluated, P1Representing said production data, P2Representing said production and management data, P3Representing the business valuation data, n1Representing the frequency of occurrence of said production data, I1Representing the priority of said production data, n2Indicating the frequency of occurrence of said production operation data, I2Representing the priority of said production data, n3Indicating the frequency of occurrence of said business valuation data, I3Representing a priority of the enterprise valuation data.
7. The method according to claim 2, wherein sending the data set to be evaluated to a preset number of the relative weight terminals specifically comprises:
and the supervision department server sends the data set to be evaluated to a preset number of relative weight terminals based on the selected probability of each relative weight terminal.
8. The method of claim 7, wherein determining the selected probability for each of the relative weight terminals comprises:
and the supervision department server determines the ratio of the effective relative weight to all the relative weights in the historical relative weight number set sent by each relative weight terminal, and determines the selected probability of each relative weight terminal based on the ratio and a preset threshold value.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, the processor implementing the method of any one of claims 1 to 8 when executing the program.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-8.
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