Medical machinery product bid invitation technical parameter examination method and system

Abstract

The invention relates to the technical field of bid invitation examination, in particular to an examination method and system for medical mechanical product bid invitation technical parameters. Comprising the steps of obtaining a bid invitation file and all bidding files; setting a plurality of technical sub-components according to a preprocessing result of the bid invitation file, and generating a parameter abnormal value of each technical sub-component according to a preset abnormal evaluation model; setting a review strategy according to a preset associated review model and all the parameter abnormal values, and generating a review result according to the review strategy; the method comprises the following steps: screening a bidding document, segmenting a plurality of technical sub-components, initially screening each technical sub-component according to a constructed abnormality evaluation model, constructing a comparison model according to a screening result, and screening all bidding documents, thereby determining whether each technical sub-component is abnormal or not. And the examination efficiency of the compliance of the technical parameters of the medical mechanical product is improved.

Description

Technical Field

[0001] This application relates to the field of tender review technology, and in particular to a method and system for reviewing the technical parameters of a medical device product tender. Background Technology

[0002] In the government procurement and hospital bidding processes for medical equipment, the review of technical parameters is a core step in ensuring the quality, performance, and suitability of the procured products. Currently, the review of technical parameters for medical devices in bidding documents mainly relies on manual review.

[0003] Existing manual review methods have several drawbacks. First, they heavily rely on the individual knowledge and experience of review experts, making them highly subjective and difficult to guarantee the consistency and objectivity of review standards. Different experts may have differing interpretations of the same technical clauses, easily leading to disputes. Second, faced with a large number of complex tender documents, manually comparing each item against the tender requirements is labor-intensive, inefficient, and prone to oversights and misjudgments due to fatigue. In particular, manual review struggles to comprehensively and accurately identify and assess the compliance of key performance indicators, the authenticity of responses, and the existence of hidden or negative deviations. Furthermore, this method lacks systematic quantitative analysis tools, making it impossible to conduct horizontal comparisons and priority rankings of numerous tender parameters, thus failing to generate data-driven decision support. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for reviewing the technical parameters of medical device products in order to solve the above-mentioned technical problems, thereby improving the efficiency and accuracy of reviewing the compliance of the technical parameters of medical device products.

[0005] In some embodiments of this application, the tender documents are screened, divided into multiple technical sub-components, and each technical sub-component is initially screened according to the constructed anomaly assessment model. A comparison model is constructed based on the screening results to screen all tender documents, thereby determining whether each technical sub-component has any anomalies, thus improving the efficiency of reviewing the compliance of technical parameters of medical device products.

[0006] In some embodiments of this application, after anomaly screening of each technical sub-component, compliance risk values ​​of each technical sub-component are generated through the tender documents, thereby achieving dual judgment of each technical sub-component. By constructing a dual review model, the accuracy of reviewing the compliance of technical parameters of medical device products is improved.

[0007] In some embodiments of this application, a method for reviewing technical parameters for bidding on medical device products is provided, including: Obtain the tender documents and all tender documents; Set multiple technical sub-components according to the preprocessing results of the tender documents, and generate parameter anomaly values for each technical sub-component according to the preset anomaly evaluation model; Set the review strategy according to the preset associated review model and all parameter anomaly values, and generate the review result according to the review strategy; Among them, generating multiple technical sub-components includes: Establish a sequence of technical sub-components A, A = (a1, a2…a i …a n ), where a i is the i-th technical sub-component; n is the number of technical sub-components.

[0008] In some embodiments of the present application, the generating of the parameter anomaly values for each technical sub-component includes: Set a i as the target technical sub-component in sequence according to the sequence of technical sub-components A; Generate the parameter anomaly value b of the target technical sub-component according to the anomaly evaluation model; b = η i *s i ; Among them, θ1 is the number of parameter evaluation indicators of the target technical sub-component; η i is the influence factor of the i-th evaluation indicator; s i is the reference value of the i-th parameter evaluation indicator in the target technical sub-component; Generate the parameter anomaly values of each technical sub-component in sequence.

[0009] In some embodiments of the present application, setting the review strategy includes: Establish a sequence of parameter anomaly values B, B = (b1, b2…b i …b n ), where b i is the parameter anomaly value of the i-th technical sub-component; n is the number of technical sub-components; Preset the parameter anomaly value threshold B1; Select the maximum value b max in the sequence of parameter anomaly values B; If b max < B1, generate a first-level review instruction; If b max > B1, generate a second-level review instruction; Among them, the second-level review instruction includes: If b i > B1, set the i-th technical sub-component as an abnormal sub-component; Judge whether each technical sub-component is an abnormal sub-component in sequence; Obtain all abnormal sub-components according to the judgment result, and construct a first-level comparison model based on all the abnormal sub-components; Judge whether to generate a first-level review instruction according to the first-level comparison model.

[0010] In some embodiments of the present application, the first-level review instruction includes: Obtain all tender documents; Generate multiple response parameter packages according to the preprocessing results of each tender document; Establish a response parameter package sequence W, W = (w1, w2…w i …w m ), where w i is the response parameter package corresponding to the i-th tender document; m is the number of tender documents; Set a i as the sub-component to be reviewed according to the technical sub-component sequence A; Generate a compliance risk value f for the sub-component to be reviewed according to all response parameter packages; Preset a compliance risk value threshold F1; If f > F1, set the sub-component to be reviewed as a risk sub-component; If f < F1, set the sub-component to be reviewed as a compliant sub-component; Generate the compliance risk values of each technical sub-component in sequence, and generate a review result according to all the compliance risk values.

[0011] In some embodiments of the present application, generating the compliance risk value f includes: f = U1 * e * k i ; where U1 is a preset first conversion coefficient; e is a risk compensation coefficient set according to the parameter abnormality value of the sub-component to be reviewed; m is the number of tender documents; k i is the fit value between the sub-component to be reviewed and the i-th response parameter package.

[0012] In some embodiments of the present application, judging whether to generate a first-level review instruction includes: Set w i as the target response package in sequence according to the response parameter package sequence W; Generate a parameter similarity value g for the target response package according to the first-level comparison model; Generate the parameter similarity values of each response parameter package in sequence; Establish a parameter similarity value sequence G, G = (g1, g2…g i …g m ), where g i is the i-th parameter similarity value; m is the number of response parameter packages; Preset a parameter similarity value threshold G1; Select the maximum value g from the sequence G of similar parameter values max ; If g max > G1, generate a first-level warning instruction; If g max < G1, generate a first-level review instruction.

[0013] In some embodiments of the present application, generating the similar parameter value g includes: g = r * β i * k 1i ; r = U2 * k 2i ; Where r is the similar compensation coefficient; θ2 is the number of abnormal sub-components in the first-level comparison model; β i is the influence factor of the i-th abnormal sub-component in the first-level comparison model; k 1i is the fit value of the i-th abnormal sub-component in the first-level comparison model of the target response package to the target response package; U2 is the preset second conversion coefficient; is the number of associated sub-components in the first-level comparison model; k 2i is the fit value of the i-th associated sub-component in the first-level comparison model to the target response package.

[0014] In some embodiments of the present application, a review system for the technical parameters of a medical device product tender is provided, including: A monitoring unit for obtaining the tender document and all bid documents; A review unit, including: A first review module for setting multiple technical sub-components according to the preprocessing result of the tender document; Establish a sequence A of technical sub-components, A = (a1, a2…a i …a n ), where a i is the i-th technical sub-component; n is the number of technical sub-components; A second review module for generating the parameter abnormality value of each technical sub-component according to the preset abnormality evaluation model; A third review module for setting a review strategy according to the preset associated review model and all parameter abnormality values, and the third review module is also used to generate a review result according to the review strategy; Among them, the second review module is also used for: Sequentially set a i as the target technical sub-component according to the sequence A of technical sub-components; Generate the parameter abnormality value b of the target technical sub-component according to the abnormality evaluation model; b = ηi *s i ; Among them, θ1 is the number of parameter evaluation indicators of the target technical sub-component; η i is the influence factor of the i-th evaluation indicator; s i is the reference value of the i-th parameter evaluation indicator in the target technical sub-component; Generate the parameter outliers of each technical sub-component in sequence.

[0015] In some embodiments of the present application, the third review module is further configured to: Establish a sequence B of parameter outliers, B = (b1, b2... b i ... b n ), where b i is the parameter outlier of the i-th technical sub-component; n is the number of technical sub-components; Preset a parameter outlier threshold B1; Select the maximum value b max ; If b max < B1, generate a first-level review instruction; If b max > B1, generate a second-level review instruction; Among them, the second-level review instruction includes: If b i > B1, set the i-th technical sub-component as an abnormal sub-component; Judge whether each technical sub-component is an abnormal sub-component in sequence; Obtain all abnormal sub-components according to the judgment results, and construct a first-level comparison model based on all abnormal sub-components; Judge whether to generate a first-level review instruction according to the first-level comparison model.

[0016] In some embodiments of the present application, the first-level review instruction includes: Obtain all tender documents; Generate multiple response parameter packages according to the preprocessing results of each tender document; Establish a sequence W of response parameter packages, W = (w1, w2... w i ... w m ), where w i is the response parameter package corresponding to the i-th tender document; m is the number of tender documents; Set a i as the to-be-reviewed sub-component according to the sequence A of technical sub-components; Generate the compliance risk value f of the to-be-reviewed sub-component according to all response parameter packages; f = U1 * e * k i ; Among them, U1 is a preset first conversion coefficient; e is a risk compensation coefficient set according to the parameter outlier of the to-be-reviewed sub-component; m is the number of tender documents; k i is the fit value between the to-be-reviewed sub-component and the i-th response parameter package; Preset compliance risk value threshold F1; If f > F1, set the to-be-reviewed sub-component as a risk sub-component; If f < F1, set the to-be-reviewed sub-component as a compliant sub-component; Generate the compliance risk values of each technical sub-component in sequence, and generate a review result based on all the compliance risk values.

[0017] Compared with the prior art, the beneficial effects of the method and system for reviewing the technical parameters of a medical device product in an embodiment of the present application are as follows: By screening the tender documents, splitting them into multiple technical sub-components, initially screening each technical sub-component according to the constructed anomaly evaluation model, and constructing a comparison model based on the screening results to screen all tender documents, it is determined whether there are anomalies in each technical sub-component, thereby improving the review efficiency of the compliance of the technical parameters of medical device products.

[0018] After completing the anomaly screening of each technical sub-component, generate the compliance risk values of each technical sub-component through the tender documents, implement a dual judgment on each technical sub-component, and improve the review accuracy of the compliance of the technical parameters of medical device products by constructing a dual review model. Description of the Drawings

[0019] Figure 1 is a schematic flowchart of a method for reviewing the technical parameters of a medical device product in a preferred embodiment of an embodiment of the present application. Detailed Embodiments

[0020] The following further describes in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present application but are not used to limit the scope of the present application.

[0021] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0023] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0024] like Figure 1 As shown in the preferred embodiment of this application, a method for reviewing the technical parameters of a medical device product in a tender includes: S101: Obtain the tender documents and all tender documents; S102: Based on the preprocessing results of the tender documents, set up multiple technical sub-components and generate abnormal parameter values ​​for each technical sub-component based on the preset abnormality evaluation model; S103: Set the review strategy based on the preset correlation review model and all parameter anomalies, and generate the review results according to the review strategy; This involves generating multiple technical sub-components, including: Establish a sequence of technical components A, A=(a1, a2, ..., a3) i …a n ), where a i Let be the i-th technical sub-component; n is the number of technical sub-components.

[0025] Specifically, by analyzing historical bidding data and combining it with semantic processing technology, a preprocessing model is constructed. This preprocessing model can extract various technical parameters and their corresponding related content (technical parameter categories, ranges, and expression methods) from the bidding documents.

[0026] Specifically, the current tender document is analyzed through a preprocessing model to generate multiple technical sub-components. Each technical sub-component represents a technical parameter package extracted from the tender document, which includes: technical parameter name, required parameter range, and name expression method.

[0027] Specifically, the technical parameters mentioned above refer to the technical parameters of various types of medical device products, including but not limited to: physical parameters, performance parameters, material property parameters, safety and electrical parameters, etc.

[0028] Specifically, generate the parameter outliers of each technical sub-component, including: Set a i as the target technical sub-component according to the sequence of technical sub-components A; Generate the parameter outlier b of the target technical sub-component according to the anomaly evaluation model; b = η i * s i ; where θ1 is the number of parameter evaluation indicators of the target technical sub-component; η i is the influence factor of the i-th evaluation indicator; s i is the reference value of the i-th parameter evaluation indicator in the target technical sub-component; Generate the parameter outliers of each technical sub-component in sequence.

[0029] Specifically, the parameter evaluation indicators include but are not limited to: the range historical deviation (the difference between the historical required range and the current required range), the expression deviation (the general expression for generating each technical parameter, judging the difference between the current expression and the general expression), the range requirement deviation (based on the actual requirements of the current tender document, judging the expected range corresponding to the technical parameters of the current technical sub-component, generating the difference between the current required range and the expected required range), etc. The parameters mapping the anomalies in the tender content are quantitatively processed so that the reference values of each parameter evaluation indicator are within the same value range, and the larger the reference value corresponding to each parameter evaluation indicator, the greater the possibility that there are anomalies in the tender content corresponding to the target technical sub-component. Specifically, the influence factors of each parameter evaluation indicator can be set according to the degree of association with the anomalies in the tender content. The greater the degree of association, the larger the value of the corresponding influence factor.

[0030] Specifically, set the review strategy, including: Establish a sequence of parameter outliers B, B = (b1, b2…b i …b n ), where b i is the parameter outlier of the i-th technical sub-component; n is the number of technical sub-components; Preset the parameter outlier threshold B1; Select the maximum value b max in the sequence of parameter outliers B; If b max < B1, generate a first-level review instruction; If b max > B1, generate a second-level review instruction; Among them, the second-level review instruction includes: If bi >B1, set the i-th technical sub-component as an abnormal sub-component; Determine whether each technical sub-component is an abnormal sub-component in turn; Based on the judgment results, obtain all abnormal sub-components, and construct a first-level comparison model based on all abnormal sub-components; The decision to generate a Level 1 review instruction is based on the Level 1 comparison model.

[0031] Specifically, when a Level 1 review instruction is generated, it indicates that there are no potential abnormal risks in the various parameters of the current tender documents.

[0032] Specifically, the parameter outlier threshold can be set based on historical parameters. If the parameter outlier of a technical component exceeds the preset threshold, it indicates that the technical component has a risk of being selectively configured (i.e., configured for a specific tender document). Timely screening and verification are necessary.

[0033] Specifically, a primary comparison model is constructed based on all selected abnormal sub-components and all associated sub-components corresponding to each abnormal sub-component. This primary comparison model is then used to screen each tender document to determine whether any abnormal risks exist.

[0034] Specifically, by analyzing all technical sub-components, it is determined whether there is a relationship between them. (If the parameters corresponding to two technologies are different parameters of the same device, then there is a relationship; otherwise, there is no relationship.) If there is a relationship, the two technical sub-components are related sub-components.

[0035] It is understandable that in the above embodiments, by screening the tender documents, dividing them into multiple technical sub-components, initially screening each technical sub-component based on the constructed anomaly assessment model, and constructing a comparison model based on the screening results to screen all tender documents, it is possible to determine whether each technical sub-component has any anomalies, thereby improving the efficiency of reviewing the compliance of technical parameters of medical device products.

[0036] In a preferred embodiment of this application, the first-level examination instruction includes: Obtain all tender documents; Multiple response parameter packages are generated based on the preprocessing results of each bid document; Establish a response parameter packet sequence W, W=(w1,w2…w i …w m ), where w i This represents the response parameter package corresponding to the i-th bid document; m is the number of bid documents. Based on the sequence of technical sub-components A, a is set sequentially. i Sub-component pending review; Generate the compliance risk value f of the sub-component to be reviewed based on all response parameter packages; Preset compliance risk value threshold F1; If f > F1, set the to-be-reviewed sub-component as a risk sub-component; If f < F1, set the to-be-reviewed sub-component as a compliant sub-component; Generate the compliance risk values of each technical sub-component in sequence, and generate a review result based on all the compliance risk values.

[0037] Specifically, the compliance risk value threshold can be set according to historical parameters. If the compliance risk value of the current technical sub-component is greater than the preset compliance risk value threshold, it indicates that the response degree to this technical sub-component in the tender document is insufficient, and there is a compliance risk (that is, it is formulated unreasonably) in the formulation of this technical sub-component.

[0038] Specifically, extract all the technical sub-components in each tender document through a preprocessing model, and generate a corresponding response parameter package based on the technical sub-components of the current tender document.

[0039] Specifically, generate the compliance risk value f, including: f = U1 * e * k i ; Among them, U1 is a preset first conversion coefficient; e is a risk compensation coefficient set according to the parameter outlier of the to-be-reviewed sub-component; m is the number of tender documents; k i is the fit value between the to-be-reviewed sub-component and the i-th response parameter package.

[0040] Specifically, make the compliance risk value fall within a preset value range through a preset first fixed coefficient, and k i The larger the value, the smaller the corresponding compliance risk value, and the mapping relationship between the two can be set according to historical parameters.

[0041] Specifically, the larger the parameter outlier, the larger the value of the corresponding risk compensation coefficient, and the mapping relationship between the two can be set according to historical parameters.

[0042] Specifically, set the corresponding fit value by analyzing the difference degree between the requirement range of the to-be-reviewed sub-component and the response range in the response parameter package. The smaller the difference degree, the larger the corresponding fit value, and the mapping relationship between the two can be set according to historical parameters.

[0043] Specifically, the larger the compliance risk value, the greater the possibility that the to-be-reviewed sub-component has a compliance risk.

[0044] It can be understood that in the above embodiments, after the abnormal screening of each technical sub-component, the compliance risk value of each technical sub-component is generated through the bidding documents, so as to achieve a dual judgment on each technical sub-component. By constructing a dual review model, the review accuracy of the compliance of the technical parameters of medical mechanical products is improved.

[0045] In a preferred embodiment of the embodiment of the present application, determining whether to generate a first-level review instruction includes: Setting w in sequence according to the response parameter packet sequence W i as the target response packet; Generating the parameter similarity value g of the target response packet according to the first-level comparison model; Generating the parameter similarity values of each response parameter packet in sequence; Establishing a parameter similarity value sequence G, G = (g1, g2... g i ... g m ), where g i is the i-th parameter similarity value; m is the number of response parameter packets; Presetting a parameter similarity value threshold G1; Selecting the maximum value g in the parameter similarity value sequence G max ; If g max > G1, generating a first-level warning instruction; If g max \>< G1, generating a first-level review instruction.

[0046] Specifically, the parameter similarity value threshold G1 can be set according to historical parameters. If the parameter similarity value of the target response packet is greater than the preset parameter similarity value threshold, it means that there is a risk of directional setting between the bidding document corresponding to the target response packet and the current bidding document, and a warning instruction needs to be generated in time for inspection. Ensure the fairness of the bidding process.

[0047] Specifically, generating the parameter similarity value g includes: g = r * β i * k 1i ; r = U2 * k 2i ; [[ID=5 5]]Where r is the similarity compensation coefficient; θ2 is the number of abnormal sub-components in the first-level comparison model; β i is the influence factor of the i-th abnormal sub-component in the first-level comparison model; k 1i is the fit value of the i-th abnormal sub-component in the first-level comparison model of the target response packet to the target response packet; U2 is the preset second conversion coefficient; is the number of associated sub-components in the first-level comparison model; k 2iThis represents the fit value between the i-th associated sub-component and the target response package in the first-level comparison model.

[0048] Specifically, by presetting a second conversion coefficient, the similarity compensation coefficient r is made to be within a preset value range, and [ k 2i The larger the value of ], the larger the corresponding similarity compensation coefficient r will be. The mapping relationship between the two can be set according to historical parameters.

[0049] Specifically, the impact factor of each abnormal sub-component can be set according to its corresponding abnormal parameter value. The larger the abnormal parameter value, the larger the value of the corresponding impact factor. The mapping relationship between the two can be set according to historical parameters.

[0050] It is understandable that in the above embodiments, after anomaly screening of each technical sub-component, compliance risk values ​​of each technical sub-component are generated through the tender documents, thereby achieving dual judgment of each technical sub-component. By constructing a dual review model, the accuracy of reviewing the compliance of technical parameters of medical device products is improved.

[0051] In another preferred embodiment of the method for reviewing technical parameters for bidding on medical device products based on any of the above preferred embodiments, this preferred embodiment provides a system for reviewing technical parameters for bidding on medical device products, including: The monitoring unit is used to obtain the tender documents and all bid documents; The review unit includes: The first review module is used to set multiple technical sub-components based on the preprocessing results of the tender documents; Establish a sequence of technical components A, A=(a1, a2, ..., a3) i …a n ), where a i Let i be the i-th technical sub-component; n is the number of technical sub-components; The second review module is used to generate abnormal parameter values ​​for each technical component based on a preset anomaly assessment model. The third review module is used to set review strategies based on the preset correlation review model and all parameter anomalies. The third review module is also used to generate review results based on the review strategies. The second review module is also used for: Based on the sequence of technical sub-components A, a is set sequentially. i For target technology components; The abnormal parameter value b of the target technical component is generated based on the abnormality assessment model; b=[ η i *s i ]; Among them, θ1 is the number of parameter evaluation indexes of the target technical sub-component; η i is the influence factor of the i-th evaluation index; s i is the reference value of the i-th parameter evaluation index in the target technical sub-component; Generate the parameter outliers of each technical sub-component in sequence.

[0052] In the preferred embodiment of this application, the third review module is further configured to: Establish a parameter outlier sequence B, B = (b1, b2... b i ... b n ), where b i is the parameter outlier of the i-th technical sub-component; n is the number of technical sub-components; Preset a parameter outlier threshold B1; Select the maximum value b max in the parameter outlier sequence B; If b max < B1, generate a first-level review instruction; If b max > B1, generate a second-level review instruction; Among them, the second-level review instruction includes: If b<​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​Among them, U1 is a preset first conversion coefficient; e is a risk compensation coefficient set according to the parameter outlier of the to-be-reviewed sub-component; m is the number of tender documents; k i is the fit value of the to-be-reviewed sub-component and the i-th response parameter package; Preset compliance risk value threshold F1; If f > F1, set the to-be-reviewed sub-component as a risk sub-component; If f < F1, set the to-be-reviewed sub-component as a compliant sub-component; Generate the compliance risk values of each technical sub-component in sequence, and generate a review result based on all the compliance risk values.

[0054] According to the first concept of the present application, by screening the tender documents, dividing multiple technical sub-components, initially screening each technical sub-component according to the constructed anomaly evaluation model, and constructing a comparison model through the screening results to screen all tender documents, so as to determine whether there are anomalies in each technical sub-component, and improve the review efficiency of the compliance of the technical parameters of medical device products.

[0055] According to the second concept of the present application, after completing the anomaly screening of each technical sub-component, generate the compliance risk values of each technical sub-component through the tender documents, implement double judgment on each technical sub-component, and improve the review accuracy of the compliance of the technical parameters of medical device products by constructing a double review model.

[0056] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application.

Claims

1. A method for reviewing technical parameters in bidding for medical device products, characterized in that, Including: Obtain the tender documents and all bidding documents; Set multiple technical sub-components according to the preprocessing results of the tender documents, and generate parameter anomaly values for each technical sub-component according to the preset anomaly evaluation model; Set the review strategy according to the preset correlation review model and all parameter anomaly values, and generate the review result according to the review strategy; Among them, generating multiple technical sub-components includes: Establish a sequence of technical components A, A=(a1, a2, ..., a3) i …a n ), where a i Let be the i-th technical sub-component; n is the number of technical sub-components.

2. The method for reviewing the technical parameters of medical device products in bidding as described in claim 1, characterized in that, The generating of the parameter anomaly values for each technical sub-component includes: Based on the sequence of technical sub-components A, a is set sequentially. i For target technology components; Generate the parameter anomaly value b of the target technical sub-component according to the anomaly evaluation model; b=[ or i *s i ]; Where θ1 represents the number of parameter evaluation indicators for the target technical component; η i Let s be the influence factor of the i-th evaluation indicator; i This is a reference value for the evaluation index of the i-th parameter in the target technical sub-component; Generate the parameter anomaly values of each technical sub-component in sequence.

3. The method for reviewing the technical parameters of medical device products in bidding as described in claim 2, characterized in that, Setting the review strategy includes: Establish a sequence of outlier parameters B, B=(b1,b2…b…). i …b n ), where b i represents the abnormal parameter value of the i-th technical sub-component; n is the number of technical sub-components; Preset the parameter anomaly value threshold B1; Select the maximum value b from the parameter outlier sequence B. max ; If b max <B1, generate a first-level review instruction; If b max >B1, Generate a Level 2 Review Instruction; Among them, the secondary review instruction includes: If b i >B1, set the i-th technical sub-component as an abnormal sub-component; Judge whether each technical sub-component is an abnormal sub-component in sequence; Obtain all abnormal sub-components according to the judgment results, and construct a primary comparison model according to all abnormal sub-components; Judge whether to generate a primary review instruction according to the primary comparison model.

4. The method for reviewing the technical parameters of medical device products in bidding as described in claim 3, characterized in that, The primary review instruction includes: Obtain all bidding documents; Generate multiple response parameter packages according to the preprocessing results of each bidding document; Establish a response parameter packet sequence W, W=(w1,w2…w i …w m ), where w i This represents the response parameter package corresponding to the i-th bid document; m is the number of bid documents. Based on the sequence of technical sub-components A, a is set sequentially. i Sub-component pending review; Generate the compliance risk value f of the sub-component to be reviewed according to all response parameter packages; Preset the compliance risk value threshold F1; If f > F1, set the sub-component to be reviewed as a risk sub-component; If f < F1, set the sub-component to be reviewed as a compliant sub-component; Generate the compliance risk values of each technical sub-component in sequence, and generate the review result according to all compliance risk values.

5. The method for reviewing the technical parameters of medical device products in bidding as described in claim 4, characterized in that, Generating the compliance risk value f includes: f=U1*e*[ k i ]; Where U1 is the preset first conversion coefficient; e is the risk compensation coefficient set according to the abnormal values ​​of the parameters of the sub-component to be reviewed; m is the number of tender documents; k i This represents the match value between the component to be reviewed and the i-th response parameter packet.

6. The method for reviewing the technical parameters of medical device products in bidding as described in claim 5, characterized in that, Judging whether to generate a primary review instruction includes: Based on the response parameter packet sequence W, set w sequentially. i For the target response packet; Generate the parameter similarity value g of the target response package according to the primary comparison model; Generate the parameter similarity values of each response parameter package in sequence; Establish a parameter similarity value sequence G, G = (g1, g2, ..., g...) i …g m ), where g i The similarity value of the i-th parameter; m is the number of response parameter packets; Preset the parameter similarity value threshold G1; Select the maximum value g from the parameter similarity value sequence G. max ; If g max >G1, generate a Level 1 warning instruction; If g max <G1, generate a first-level review instruction.

7. The method for reviewing the technical parameters of medical device products in bidding as described in claim 6, characterized in that, Generating the parameter similarity value g includes: g=r*[ b i *k 1i ]; r=U2*[ k 2i ]; Where r is the similarity compensation coefficient; θ2 is the number of anomalous sub-components in the first-level comparison model; β i k represents the influence factor of the i-th anomalous sub-component in the first-level comparison model. 1i U1 represents the matching value between the i-th abnormal sub-component and the target response package in the first-level comparison model of the target response package; U2 is the preset second conversion coefficient. k represents the number of associated sub-components in the first-level comparison model. 2i This represents the fit value between the i-th associated sub-component and the target response package in the first-level comparison model.

8. A system for reviewing technical parameters for bidding on medical device products, employing the method for reviewing technical parameters for bidding on medical device products as described in any one of claims 1-7, characterized in that, Including: The monitoring unit is used to obtain the tender documents and all bidding documents; The review unit includes: The first review module is used to set multiple technical sub-components according to the preprocessing results of the tender documents; Establish a sequence of technical components A, A=(a1, a2, ..., a3) i …a n ), where a i Let i be the i-th technical sub-component; n is the number of technical sub-components; The second review module is used to generate the parameter anomaly values of each technical sub-component according to the preset anomaly evaluation model; The third review module is used to set the review strategy according to the preset correlation review model and all parameter anomaly values, and the third review module is also used to generate the review result according to the review strategy; Among them, the second review module is also used for: Based on the sequence of technical sub-components A, a is set sequentially. i For target technology components; Generate the parameter anomaly value b of the target technical sub-component according to the anomaly evaluation model; b=[ or i *s i ]; Where θ1 represents the number of parameter evaluation indicators for the target technical component; η i Let s be the influence factor of the i-th evaluation indicator; i This is a reference value for the evaluation index of the i-th parameter in the target technical sub-component; Generate the parameter anomaly values of each technical sub-component in sequence.

9. The review system for technical parameters of medical device products in bidding as described in claim 8, characterized in that, The third review module is also used for: Establish a sequence of outlier parameters B, B=(b1,b2…b…). i …b n ), where b i represents the abnormal parameter value of the i-th technical sub-component; n is the number of technical sub-components; Select the maximum value b from the parameter outlier sequence B. max ; If b max <B1, generate a first-level review instruction; If b max >B1, Generate a Level 2 Review Instruction; Preset the parameter anomaly value threshold B1; If b i >B1, set the i-th technical sub-component as an abnormal sub-component; Among them, the secondary review instruction includes: Judge whether each technical sub-component is an abnormal sub-component in sequence; Obtain all abnormal sub-components according to the judgment results, and construct a primary comparison model according to all abnormal sub-components; 10. The review system for technical parameters of medical device products in bidding as described in claim 9, characterized in that, Judge whether to generate a primary review instruction according to the primary comparison model. The primary review instruction includes: Obtain all bidding documents; Establish a response parameter packet sequence W, W=(w1,w2…w i …w m ), where w i This represents the response parameter package corresponding to the i-th bid document; m is the number of bid documents. Based on the sequence of technical sub-components A, a is set sequentially. i Sub-component pending review; Generate multiple response parameter packages according to the preprocessing results of each bidding document; f=U1*e*[ k i ]; Where U1 is the preset first conversion coefficient; e is the risk compensation coefficient set according to the abnormal values ​​of the parameters of the sub-component to be reviewed; m is the number of tender documents; k i The matching value between the sub-component to be reviewed and the i-th response parameter packet; Generate the compliance risk value f of the sub-component to be reviewed according to all response parameter packages; Preset the compliance risk value threshold F1; If f > F1, set the sub-component to be reviewed as a risk sub-component; If f < F1, set the sub-component to be reviewed as a compliant sub-component; Generate the compliance risk values of each technical sub-component in sequence, and generate the review result according to all compliance risk values.

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